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Knowledge Modellingand
Big Data Analyticsin
Healthcare
Knowledge Modellingand
Big Data Analyticsin
Healthcare
Advances and Applications
Edited by
Mayuri Mehta,
Kalpdrum Passi,
Indranath Chatterjee, and
Rajan Patel
First edition published 2022
by CRC Press
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and by CRC Press
2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN
© 2022 selection and editorial matter, Mayuri Mehta, Kalpdrum Passi, Indranath Chatterjee, and
Rajan Patel; individual chapters, the contributors
CRC Press is an imprint of Taylor & Francis Group, LLC
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Library of Congress Cataloging-in-Publication Data
Names: Mehta, Mayuri, editor.
Title: Knowledge modelling and big data analytics in healthcare : advances and applications /
edited by Mayuri Mehta, Kalpdrum Passi, Indranath Chatterjee, and Rajan Patel.
Description: First edition. | Boca Raton : CRC Press, 2022. | Includes bibliographical references
and index.
Identiers: LCCN 2021025540 (print) | LCCN 2021025541 (ebook) | ISBN 9780367696610
(hardback) | ISBN 9780367696634 (paperback) | ISBN 9781003142751 (ebook)
Subjects: LCSH: Medical informatics. | Medical care—Information technology. | Big data.
Classication: LCC R858 .K66 2022 (print) | LCC R858 (ebook) | DDC 610.285—dc23
LC record available at https://lccn.loc.gov/2021025540
LC ebook record available at https://lccn.loc.gov/2021025541
ISBN: 978-0-367-69661-0 (hbk)
ISBN: 978-0-367-69663-4 (pbk)
ISBN: 978-1-003-14275-1 (ebk)
DOI: 10.1201/9781003142751
Typeset in Times
by Apex CoVantage, LLC
v
Contents
Preface.......................................................................................................................ix
Editors .......................................................................................................................xi
Contributors ........................................................................................................... xiii
SECTION I Big Data in Healthcare
Chapter 1 Intelligent Healthcare Systems: A Design Overview
of Existing Systems and Technologies .................................................3
S. Saravanan, R. Saminathan, and P. Anbalagan
Chapter 2 An Overview of Big Data Applications in Healthcare:
Opportunities and Challenges............................................................ 21
Akansha Gautam and Indranath Chatterjee
Chapter 3 Clinical Decision Support Systems and Computational
Intelligence for Healthcare Industries ................................................37
Swapnil Singh, Ameyaa Biwalkar, and Vidhi Vazirani
Chapter 4 Proposed Intelligent Software System for Healthcare
Systems Using Machine Learning .....................................................65
Manu Banga
SECTION II Medical Imaging
Chapter 5 Diagnosis of Schizophrenia: A Study on Clinical and
Computational Aspects ......................................................................89
Indranath Chatterjee and Khushboo Mittal
Chapter 6 Deep Learning in Medical Imaging................................................. 107
Arjun Sarkar
Chapter 7 Integrated Neuroinformatics: Analytics and Application................ 133
Rajan Patel, Rahul Vaghela, Madhuri Chopade,
Prakash Patel, and Dulari Bhatt
vi Contents
Chapter 8 A Fast and Reliable Detection System for Lung Cancer
Using Image-Processing Techniques ............................................... 145
Anindya Banerjee and Himadri Sekhar Dutta
SECTION III Computational Genomics
Chapter 9 Improved Prediction of Gene Expression of Epigenomics
Data of Lung Cancer Using Machine Learning and Deep
Learning Models .............................................................................. 165
Kalpdrum Passi, Zhengxin Shi, and Chakresh Kumar Jain
Chapter 10 Genetic Study of Schizophrenia and Role of Computational
Genomics in Mental Healthcare....................................................... 183
Namrata Jawanjal and Indranath Chatterjee
Chapter 11 Prediction of Disease–lncRNA Associations via Machine
Learning and Big Data Approaches.................................................203
Mariella Bonomo, Armando La Placa, and
Simona Ester Rombo
SECTION IV Applications in Clinical Diagnosis
Chapter 12 Tracking Slow Modulations of Effective Connectivity
for Early Epilepsy Detection: A Review.......................................... 229
Vishwambhar Pathak, Praveen Dhyani, Prabhat Mahanti,
and Vivek Gaur
Chapter 13 An Application for Predicting Type II Diabetes Using
an IoT Healthcare Monitoring System ............................................. 257
Dalvin Vinoth Kumar A, Cecil Donald A, and
Margaret Mary T
vii Contents
SECTION V Issues in Security and Informatics
in Healthcare
Chapter 14 A Conceptual Model for Assessing Security and Privacy
Risks in Healthcare Information Infrastructures:
The CUREX Approach....................................................................285
Georgia Kougka, Anastasios Gounaris, Apostolos
Papadopoulos, Athena Vakali, Diana Navarro Llobet, Jos
Dumortier, Eleni Veroni, Christos Xenakis, and Gustavo
Gonzalez-Granadillo
Chapter 15 Data Science in Health Informatics .................................................299
Behzad Soleimani Neysiani, Nasim Soltani, Saeed Doostali,
Mohammad Shiralizadeh Dezfoli, Zahra Aminoroaya, and
Majid Khoda Karami
Index ...................................................................................................................... 3 41
ix
Preface
We are living in an era of information and knowledge modelling through big data
analytics. Big data analytics has been widely applied to elds such as agriculture,
e-commerce, education, nance, manufacturing industry and sports to extract infor-
mation and knowledge from a voluminous amount of data. An enormous volume
of structured as well as unstructured data produced by the healthcare industry also
offers ample opportunities to convert them into real and actionable insights, thereby
providing new understanding and ways for quicker and enhanced healthcare ser-
vices. Due to the increasing availability of electronic healthcare data and the rapid
progress of analytic techniques, much research is being carried out in this area.
Popular methods include machine learning/deep learning for structured data and
natural language processing for unstructured data. Guided by relevant clinical ques-
tions, knowledge modelling through big data analytic techniques can unlock clini-
cally relevant information hidden in the massive amount of data, which, in turn, can
assist clinical decision-making.
This book provides state-of-the-art research in the healthcare sector, along with
related subdomains, to ensure completeness and quality. Specically, it highlights
modern study and developments, challenges, opportunities and future research
directions in healthcare. The focus of the book is on automated analytical techniques
used to extract new knowledge from a huge amount of medical data for healthcare
applications. The book connects four contemporary areas of research: articial intel-
ligence, big data analytics, knowledge modelling and healthcare.
The book is structured into ve sections that cover key areas of healthcare, such
as big data in healthcare, medical imaging, computational genomics, applications in
clinical diagnosis and issues in security and informatics in healthcare.
Section I—Big Data in Healthcare. This section focuses on healthcare systems,
opportunities and challenges, clinical decision support systems and intelligent soft-
ware systems.
Section II—Medical Imaging. This section presents the latest research on the
application of articial intelligence on various applications of image analysis for
the diagnosis and prediction of severe diseases such as cancer, schizophrenia and
neuroinformatics.
Section III—Computational Genomics. Genetics and genomics are emerging
sub-domains of healthcare. Genetics and genomics datasets are complex and large,
wherein deep learning algorithms have a plethora of uses. This section reports prog-
ress in genetics and genomics, including genetic diagnosis, genotype–phenotype
correlations and sequencing of the human genome.
Section IV—Applications in Clinical Diagnosis. The purpose of this section is to
report the clinical applications of tracking modulations in epilepsy and the use of the
Internet of Things for monitoring diabetes.
Section V—Issues in Security and Informatics in Healthcare. This section pre-
sents security and privacy issues in healthcare information systems and the use of
data science in healthcare.
x Preface
This book is primarily intended for data scientists, machine learning enthusiasts,
industry professionals from the healthcare sector, researchers, doctors, students and
academicians. By reading this book, they will gain essential insights on modern big
data analytic techniques needed to advance innovation in both the healthcare indus-
try and patients.
xi
Editors
Mayuri Mehta, PhD, is a passionate learner, teacher, and researcher. She is a
professor in the Department of Computer Engineering, Sarvajanik College of
Engineering and Technology, Surat, India. She earned her PhD in computer engi-
neering from Sardar Vallabhbhai National Institute of Technology, India. Her areas
of teaching and research include data science, machine learning and deep learning,
health informatics, computer algorithms, and Python programming. Dr Mehta has
worked on several academic assignments in collaboration with professors from uni-
versities across the globe. Her 20 years of professional experience includes several
academic and research achievements along with administrative and organizational
capabilities. She has also co-edited a book, Tracking and Preventing Diseases using
Articial Intelligence. With the noble intention of applying her technical knowledge
for societal impact, she is working on several research projects in the healthcare
domain in association with doctors engaged in private practice and doctors work-
ing with medical colleges, which reect her research outlook. Dr Mehta is an active
member of professional bodies such as the IEEE, Computer Society of India (CSI)
and the Indian Society for Technical Education (ISTE).
Kalpdrum Passi, PhD, earned his PhD in parallel numerical algorithms from the
Indian Institute of Technology, Delhi, India in 1993. He is an associate professor
in the Department of Mathematics & Computer Science at Laurentian University,
Ontario, Canada. He has published many papers on parallel numerical algorithms in
international journals and conferences. Dr Passi has collaborated in research work
with faculty in Canada and the United States, and the work was tested on the CRAY
XMP’s and CRAY YMP’s. He transitioned his research to web technology and, more
recently, has been involved in machine learning and data mining applications in
bioinformatics, social media, and other data science areas. His research in bioinfor-
matics has been on improving the accuracy of predicting diseases such as different
types of cancer using microarray data. Dr Passi has published several papers related
to prediction of cancer using microarray data and epigenomic data. He obtained
funding from the Natural Sciences and Engineering Research Council of Canada
and Laurentian University for his research. He is a member of the Association of
Computing Machinery and the IEEE Computer Society.
Indranath Chatterjee, PhD, is a professor in the Department of Computer
Engineering at Tongmyong University, Busan, South Korea. He earned his PhD in
computational neuroscience from the Department of Computer Science, University
of Delhi, Delhi, India. His research areas include computational neuroscience, medi-
cal imaging, data science, machine learning, and computer vision. Dr Chatterjee
is the author of four textbooks on computer science and has published numerous
scientic articles in renowned international journals and conferences. He is cur-
rently serving as a chief section editor of the Neuroscience Research Notes journal
and as a member of the advisory board and editorial board of various international
xii Editors
journals and open-science organizations worldwide. Dr Chatterjee is presently work-
ing with government and nongovernment organizations as principal/co-principal
investigator on several projects related to medical imaging and machine learning for
a broader societal impact, in collaboration with more than 15 universities globally.
He is an active professional member of the Association of Computing Machinery
(USA), the Organization of Human Brain Mapping (USA), Federations of European
Neuroscience Society (Belgium), the International Association of Neuroscience
(India), and the International Neuroinformatics Coordinating Facility (Sweden).
Rajan Patel, PhD, is a professor at the Gandhinagar Institute of Technology,
Gandhinagar, Gujarat, India. He earned a PhD in computer engineering from
R. K. University, Rajkot, India, and an M.Tech. in computer engineering from
S. V. National Institute of Technology (NIT), Surat, India. Dr Patel has more than
16 years of teaching experience in the elds of computer science and engineering
and research experience mainly in the domain of networking, security, and intel-
ligent applications. He has more than 51 collaborative publications in journals and
conferences and presented 17 articles in national/international conferences including
IEEE, Science Direct, Springer, and Elsevier. As a co-editor, he has published an
edited international book, Data Science and Intelligent Applications, in Springer’s
Lecture Notes on Data Engineering and Communication Technologies series. He
also worked for Information Security Education & Awareness Program-sponsored
and Ministry of Human Resource and Development-funded project during his post-
graduation period at NIT, Surat, India. Dr Patel is a member of several professional
bodies, including CSI, the International Society for Technology in Education, and
the Universal Association of Computer and Electronics Engineers. He has also
received numerous awards, honors, and certicates of excellence. His main area of
interest includes articial intelligence, data science, and intelligent communication
and its security.
xiii
Contributors
Zahra Aminoroaya, Department of Computer Engineering, Allameh Naieni Higher
Education Institute.
Dr. P. Anbalagan is an assistant professor in Department of Computer Science and
Engineering, Annamalai University with over 12 years of experience. He completed
his PhD in computer science and engineering in 2016. He published several papers
in international conferences and journals. His research interests include data mining,
big data analytics, mobile ad hoc networks and software engineering.
Anindya Banerjee, Master’s student at Friedrich-Alexander-Universität Erlangen-
Nürnberg, Germany. Previously he has worked in Capgemini India private limited
and holds a Master’s degree (M.Tech) in communication engineering from Kalyani
Government Engineering College, India. He has few years of both research and
industrial experience.
Manu Banga, researcher at Amity University Uttar Pradesh Noida. His area of
research is articial intelligence and data mining. He earned his PhD in computer
science and engineering from ASET Amity University Noida, M.Tech in computer
science and engineering from CDAC Noida and B.Tech from Gurukul Kangri
Vishwavidyalaya Haridwar. He published 13 research papers in SCI and Scopus
journals, 2 patents and 2 books.
Dulari Bhatt, is an assistant professor in GTU afliated Engineering College in
Gujarat. She has 10 years of teaching experience to UG and PG students. She has
published a book titled Big Data Analytics with TechKnowledge Publication. She
has also published more than 15 research papers in high quality journals. Machine
learning, deep learning and computer vision are her areas of expertise.
Ameyaa Biwalkar is currently working as an assistant professor in the Computer
Engineering Department at Mukesh Patel School of Technology Management and
Engineering, NMIMS University. She has co-authored various papers in reputed
conferences and journals. Her research interests include data mining, natural lan-
guage processing, and machine learning. She has completed her Master’s in informa-
tion technology and is pursuing her PhD.
Mariella Bonomo is a PhD student in information and communication technologies
(ICT) at University of Palermo. She earned her MS degree in computer science in
2019. Her main research interests are in big data, bioinformatics, precision medicine
and data integration. In particular, she works on the analysis of complex networks in
different application contexts.
xiv Contributors
Indranath Chatterjee is a professor in the Department of Computer Engineering,
Tongmyong University, South Korea. He earned his PhD from University of Delhi, India.
His areas of interest include schizophrenia, computational neuroscience, medical imag-
ing, machine learning, natural language processing, and deep learning. He is an author
of 5 textbooks and more than 40 scientic papers.
Mrs. Madhuri Chopade is a research scholar in Indus University and an assis-
tant professor at Gandhinagar Institute of Technology, Gandhinagar since February
2010. She has 13 years of academic experience and her areas of interest include data
science machine learning, deep learning, and image processing. She has published
ve papers in international and two papers in national journals.
Dr. Praveen Dhyani is an honorary professor of computer science at Banasthali
Vidyapith. Served in various academic and administrative capacities in BITS Pilani,
BIT Mesra, Ranchi and Banasthali Vidyapith.
Dr. Cecil Donald, is an assistant professor in the Department of Computer Science,
CHRIST (Deemed to be University), Bengaluru, Karnataka. He has a Master’s degree in
software engineering and a PhD in computer science. He holds an Australian Patent and
a copyright to his credit. His research interests include cloud computing, IoT and big data.
Saeed Doostali earned a PhD in software engineering from University of Kashan. He
was the head of the computer department at Mahallat Institute of Higher Education
from 2014 to 2019. He is currently the manager of the Soft Computing Journal,
Kashan, Iran. His research interests include soft computing and its applications in
different elds of science, especially computers and medicine.
Jos Dumortier is an honorary professor at the Faculty of Law of KU Leuven. He
is the founder and for 24 years was the rst director of the Interdisciplinary Centre
for Law and ICT (KU Leuven – ICRI, today CITIP). He is the senior editor of the
International Encyclopaedia of Cyber Law. Since 2014, he is a full-time partner of
TIMELEX, the Brussels-based law rm specialising in ICT and data protection law.
Himadri Sekhar Dutta Department of Electronics and Communication Engineering,
Kalyani Government Engineering College Kalyani, India.
Vivek Gaur, assistant professor at BIT Mesra Jaipur Campus with over 17 years
of teaching experience. He earned his PhD from Banasthali University and has
a Master’s degree from Birla Institute of Technology and Science (BITS), Pilani.
Currently, he is an assistant professor in the Computer Science Department at BIT,
Mesra Jaipur Campus. His research interests include distributed systems, cloud com-
puting and data analytics technologies.
Akansha Gautam is a guest lecturer in Miranda House, University of Delhi. She
has authored several papers in the eld of big data and cloud computing. She earned
a Master’s degree in computer science from University of Delhi, New Delhi.
xv Contributors
Dr. Gustavo Gonzalez-Granadillo is a research engineer at Atos Research and
Innovation Spain, where he participates as a project director and proposal coordina-
tor on various EU initiatives related to cybersecurity. His research interests include
risk assessment methods, SIEM, critical infrastructures, articial intelligence, and
attack impact models.
Anastasios Gounaris is an associate professor in the Department of Informatics
of the Aristotle University of Thessaloniki, Greece. He earned his PhD from the
University of Manchester (UK) in 2005. He is involved in several data science proj-
ects and his research interests are in the areas of big data management and analytics.
Chakresh Kumar Jain earned his PhD in the eld of bioinformatics from Jiwaji
University, Gwalior, India, focusing on computational designing of non-coding
RNAs using machine learning methods. He is an assistant professor, Department
of Biotechnology, Jaypee Institute of Information Technology, Noida, India. He
is CSIR-UGC-NET [LS] qualied and a member of International Association of
Engineers (IAENG)
Namrata Jawanjal, is a PhD student at Pukyong National University, Busan,
Republic of Korea. She has completed her master’s degree in medical biotechnol-
ogy. Her areas of interest are genetics, expression of genes, RNA mis-splicing, single
nucleotide polymorphism, and designing a genetic marker kit.
Majid Khoda Karami is an English Teacher. He is currently working at IELTS
TEHRAN. He earned an MA in general linguistics. He graduated from the University
of Birjand.
Georgia Kougka is a postdoc in Aristotle University of Thessaloniki and her
research interests lie in the eld of data-centric ow modelling, optimization and
execution, but also in business process optimization. Other research interests are
the data pre-processing for predictive maintenance purposes in Industry 4.0 sce-
narios. Her published research results provide an advanced insight in data analytics
optimization.
Dr. A. Dalvin Vinoth Kumar, assistant professor, Department of Computer
Science, Kristu Jayanti College Bengaluru, has 4 years of teaching and 7 years of
research experience. He earned his PhD in IoT from Bharathidasan University. His
areas of interest include MANET, IoT, routing protocols, computer vision and IoT
data analytics. He led three Indian Patents and one computer software copyright
granted.
Dr. Prabhat K. Mahanti is a professor of computer science at the University of
New Brunswick, Canada. He has 43 years of teaching and research experience
and has published more than 200 papers in international journals and guided
several graduate students. He worked as a reviewer for international conferences
and editorial board member of several international journals of repute.
xvi Contributors
Khushboo Mittal has earned her M. Sc. in computer science from the Department
of Computer Science, University of Delhi, India. Her research areas of interest
include schizophrenia, machine learning, and image processing.
Diana Navarro-Llobet, is the head of Research and Innovation at FPHAG and
chair of the board of the Spanish network REGIC. She is an evaluator expert for the
European Commission and other agencies. She earned a PhD in chemistry and has
worked as a scientist before turning to research and innovation management.
Apostolos N. Papadopoulos is an associate professor of computer science at the
School of Informatics of Aristotle University of Thessaloniki (AUTH). He earned
his 5-year Diploma Degree in computer engineering and informatics from the
University of Patras, and his PhD in Informatics from the School of Informatics
(AUTH). His research interests include data management, data mining and big data
analytics.
Kalpdrum Passi is an associate professor, Department of Mathematics & Computer
Science, at Laurentian University, Ontario, Canada. He has been involved in machine
learning and data mining applications in bioinformatics, social media and other data
science areas. His research in bioinformatics has focused on improving the accuracy
of predicting cancer using microarray data.
Prof. Prakash Patel, is an assistant professor in information technology at GIT.
Prof. Patel earned a master’s degree in computer science & engineering from Nirma
University. His areas of interest are software dened networking, DBMS, system
programming, and big data analytics. He has published four international and two
national papers. He attended various conferences, STTP, and workshops. He pub-
lished 10 articles in technical magazines.
Rajan Patel is a professor at Gandhinagar Institute of Technology with over 17 years
of experience. He earned a PhD from RK University, Rajkot, and M.Tech. from NIT
Surat. He worked under the ISEAP sponsored project. He has more than 53 publica-
tions in conferences and journals. He is a co-author of edited books and he has two
granted patents.
Vishwambhar Pathak is an assistant professor in the Department of CSE. He is
working on a project funded by the government of India on the topic of deep learning
with application to biophysiological signals. Dr. Pathak earned a doctoral degree in
computer science along with Master’s degree in computer applications and MSc in
physics.
Armando La Placa is a PhD student in information and communication technolo-
gies (ICT) at University of Palermo. His research topics include the study of bio-
informatics problems and the extraction of information from heterogeneous data.
He is interested in developing solutions using big data technologies such as NoSQL
databases and distributed computational frameworks (e.g., Apache Spark).
Contributors xvii
Simona E. Rombo is associate professor of computer science at University of
Palermo. Her research interests span from bioinformatics to big data management
and articial intelligence. She leads several projects in these elds. She is CEO and
co-founder of a startup working on decision support for precision medicine.
Dr. R. Saminathan is an associate professor in the Department of Computer Science
and Engineering, Annamalai University with over 21 years of experience. He earned
his PhD in computer science and engineering in 2012. He published several papers
in international conferences and journals. His research interests include computer
networks, network security, mobile ad hoc networks and big data.
Dr. S. Saravanan is an assistant professor in the Department of Computer Science
and Engineering, Annamalai University with over 15 years of experience. He earned
his PhD in computer science and engineering in 2015. He published several papers
in international conferences and journals. His research interests include computer
networks, network security, mobile ad hoc networks and big data.
Arjun Sarkar FH Aachen University of Applied Sciences, Germany.
Zhengxin Shi is a graduate student in computational sciences. He has completed his
Master’s program and is currently working as a software developer in an IT company
in China.
Mohammad Shiralizadeh Dezfoli earned his BSc. and MSc. in Software Engineering
from Shahid Ashra Esfahani University, Esfahan, Iran, in 2016 and 2018, respec-
tively. His thesis was on educational data mining. He also studied and published
several papers in the realms of robotics, IoT, embedded systems, and data mining.
Swapnil Singh is currently pursuing a B Tech in computer engineering and MBA in
technology management from Mukesh Patel School of Technology Management and
Engineering, NMIMS University, Mumbai, India. He is also working on a B Tech
Honors in articial intelligence and machine learning from IBM. His research inter-
ests include machine learning, deep learning, medical imaging and natural language
programming.
Dr. Behzad Soleimani Neysiani earned his BSc and MSc in software engineering
from the Islamic Azad University of Najafabad in 2009 and 2012, and his PhD in the
same eld from the University of Kashan in 2019. He published more than 20arti-
cles on software and articial intelligence-related elds such as software testing and
modelling, distributed systems, text, and data mining.
Nasim Soltani earned her B.Sc. in computer engineering from Payam Noor
University, and her M.Sc. from Allame Naeini Higher Education Institute, in 2013
and 2016 respectively. She has published several papers in cloud computing eld
in many conferences and journals. Her main research interests include distributed
systems, data mining and articial intelligence.
xviii Contributors
Dr. Margaret Mary T, assistant professor at Kristu Jayanti College (Autonomous),
Bangalore, India, She has authored various papers and book chapters in reputed
national and international journals in Scopus, and was granted two patent from India
and Australia. Additionally, she was deputy custodian in BU A.Y 2018–2019 for
MCA and MSc, and was a BOE Member for BU and North University in A.Y 2019–
2020 (MCA and MSc).
Mr. Rahul A. Vaghela is pursuing his PhD from PARUL University and work-
ing as assistant professor & Head of IT Department at Gandhinagar Institute of
Technology, Moti Bhoyan. He has more than 12 years of academic and 3 years of
industrial experience. He published articles in eight research publications. He was
awarded with Pedagogical Innovation Award from Gujarat Technological University
and Letter of Appreciation from IIT Bombay for Open Source Technology Club
(OSTC) coordinator.
Athena Vakali, is a professor at the School of Informatics, Aristotle University of
Thessaloniki, Greece, and is also leading the Laboratory on Data and Web science.
She has co-authored 5 books, 15 book chapters and more than 160 papers in high
quality journals and conferences. She has coordinated and participated in more than
25 national, EU and international research and innovation projects.
Vidhi Vazirani is currently pursuing a B.Tech in computer engineering along with
an MBA in technology management from Mukesh Patel School of Technology
Management and Engineering NMIMS University, Mumbai, India. She is also work-
ing on a B.Tech Honors in articial intelligence and machine learning from IBM.
Her research interests include machine learning, deep learning and natural language
programming.
Eleni Veroni is a research associate and member of the Systems Security Laboratory
at the University of Piraeus, Greece. She earned an M.Sc. degree in Digital Systems
Security from the same university. She has participated in several EU-funded
research and innovation projects. Her research interests lie in the areas of data secu-
rity and privacy.
Prof. Christos Xenakis is a faculty member in the Department of Digital Systems
of the University of Piraeus, Greece, since 2007, where he currently is a professor,
a member of the Systems Security Laboratory and the director of the Postgraduate
Degree Programme on Digital Systems Security. He has authored more than 90
papers in peer-reviewed journals and international conferences.
Section I
Big Data in Healthcare
3
Intelligent Healthcare
1 Systems
A Design Overview
of Existing Systems
and Technologies
S. Saravanan, R. Saminathan, and P. Anbalagan
CONTENTS
1.1 Introduction ......................................................................................................3
1.2 Contributions in This Chapter ..........................................................................6
1.3 Healthcare Monitoring in IoT Using Big Data Analytics.................................6
1.4 Industrial IoT-Enabled Framework for Health Using the Cloud.......................7
1.5 Patient Health Monitoring System Based on the IoT........................................9
1.6 IoT Smart Health Monitoring System...............................................................9
1.7 IoT-Based Healthcare Monitoring System...................................................... 11
1.8 Monitoring of Human Body Signal Based on LoRa Wireless
Network System.............................................................................................. 12
1.9 A Framework for Healthcare System Using Cloud Computing ..................... 13
1.10 Wearable Devices ........................................................................................... 14
1.11 Precision Medicine and Health State Monitoring through
Wearable Devices ........................................................................................... 15
1.12 Conclusion ...................................................................................................... 17
References................................................................................................................ 18
1.1 INTRODUCTION
As the world population increases, chronic diseases and critical health issues also
increase day by day. To seek out health problems, a smart health monitoring sys-
tem should be developed throughout the world. In today’s world, people are busy
with their work schedules; they do not have time to visit hospitals or healthcare pro-
viders. This affects people’s health status and creates greater problems. In order to
improve the health status, a smart healthcare monitoring system should be installed
for periodic health checkups (Al Brashdi et al. 2018). A smart system sends an
alert to the concerned healthcare provider or hospital or to the concerned patient
about the patient’s health status. The smart healthcare system (Figure 1.1) monitors
the patient’s temperature, blood pressure, heart rate, respiration rate, oxygen rate
DOI: 10.1201/9781003142751-2
4 Knowledge Modelling and Big Data Analytics
Healthcare
protocols
Personal health
record
Disease
classification
and indexing
Intelligent Healthcare system
Cloud computing
Internet of
medical things
and sensors
Medical
document
storage and
security
FIGURE 1.1 Intelligent Healthcare.
through SpO2 and the like. If there is a change in these parameters, the alert short
message service (SMS) will be transferred to the patient’s caretaker or doctors or
healthcare provider. For an efcient, smart healthcare system, the Internet of Things
(IoT) is implemented. Generally, IoT is an efcient and promising technology in this
modern world. The IoT consists of a collection of physical devices, such as sensors,
computing devices, and storage devices, that work based on the data collected from
the sensors, and the data are stored in the form of a database (Dineshkumar et al.
2016; Hossain et al. 2016; Medvediev et al. 2018). The stored database in the IoT can
be accessed and controlled anywhere anytime by Internet technologies. Table 1.1
depicts the pros and cons of the present healthcare system.
The IoT framework consists of three architectural layers, namely, (1) the IoT device
layer, which is on the client side; (2) the IoT gateway layer, which is on the server side;
and (3) the IoT platform layer, which works as a pathway between the clients and the
operator. The building blocks of the IoT are sensors, actuators, data acquisition devices,
TABLE 1.1
Pros and Cons of Present-Day Healthcare System
S. No Pros Cons
1. Downsizing Hospital downsizing
2. Cost reduction Staff workload
3. Reduce the cost of labor No foresight in the management
4. Reduced budget No adequate care for patient
5 Intelligent Healthcare Systems
preprocessing devices and cloud platform. The sensors in the system can be used either
as an embedded device or as self-supporting devices that are used to collect the tele-
metric data. There is another device called an actuator. Actuators are the devices that
are used to convert physical movement of data into another form. When actuators are
connected with sensors, the data collected are converted into physical data. Based on
the collected data, the IoT device analyzes the data and orders the actuators to perform
a certain task or activity. The data acquired from the sensors are data and resource con-
strained, and the data consume a large amount of power, which leads to device failure.
Even though the sensors and the actuators are acting together (Ananth et al. 2019), the
data acquisition process is the most important stage in the IoT, where the collection of
data, ltration and storage-based platforms like cloud computing are used. Gateways in
the IoT are used to convert the sensor data into some format that can be transferrable to
some other format. Gateways are used to control, transfer, lter and select data to mini-
mize the storage level in the cloud-based platform. Gateways act as local preprocessors
of sensor data that are ready for further processing. The gateway acts as a security. The
gateway is accountable for monitoring the data stream on both the client and server
sides with the proper encryption and authentication. In the IoT, the data transfer speed
can be enhanced by using edge computing (Shahidul Islam et al. 2019). Edge computing
is used to speed up the data analysis in the IoT platform. In edge computing, the base
station is close to the server, and it is easy to collect and process the data. This, in turn,
reduces the power consumption of the network infrastructure (Wanjari et al. 2016).
In the IoT, the sensors and gateway act as the neurons and backbone of the system.
The cloud acts as the brain of the system. To store and access the massive amount of
data, a cloud-based platform or data centers are used. Present-day remote monitor-
ing systems apply deep learning and articial intelligence to categorize and classify
health data. The IoT has a variety of applications, such as agriculture monitoring,
health monitoring, trafc monitoring, smart grid and energy-saving management,
water supply management, eet management, maintenance management and the like
(Cheol Jeong et al. 2018).
In the healthcare industry, the IoT plays an important and crucial role. The auto-
mated features of the IoT in the medical industry are called the Internet of Medical
Things. There are two types of applications in the health industry: (1) E-health and
(2) M-health. Generally, the Internet of Medical Things is dened as a group of med-
ical devices like blood pressure sensors, pressure sensors and the like that connect
to a network of computers through gateways. E-health is implemented with the help
of electronic devices and communication. M-health, or mobile health, is dened as
the sharing of medicine prescriptions through mobile applications. The basic device
used in IoT is radio-frequency identication (RFID). The RFID automatically iden-
ties and tracks an object with a tag. In the RFID method, the chip is enabled with
an RFID tag to support the patients. The chip is implanted in the person’s body to
monitor the health parameters.
In Software Development Life Cycle technology, two different phases are used:
(1) the planning phase and (2) the implementation phase. It is a type of waterfall
methodology. In this method, each stage is parted into small groups. Testing and
implementation are carried out through each stage. Five different phases are imple-
mented: (1) Planning: In planning phase, the requirements are gathered to reach
6 Knowledge Modelling and Big Data Analytics
the goal. (2) Analysis: There are two steps in this stage: the preliminary stage and
system analysis. In the preliminary stage, the problem, objectives and goals should
be dened. In system analysis, the information is gathered, interpreted and used
for a diagnosis. (3) Design: In the design stage, the perception of each stage is
visible to the users. (4) Implementation: In this stage, the program is developed.
(5) Maintenance: The performance of the system is monitored continuously.
1.2 CONTRIBUTIONS IN THIS CHAPTER
• This chapter discusses state-of-the-art healthcare systems. It also explores the
recent technologies used in healthcare systems.
• Furthermore, cloud platforms, which have deep support towards data popula-
tion and organization, are also discussed.
• The industrial healthcare platforms used in the present-day market are also
discussed, along with geriatric healthcare services.
1.3 HEALTHCARE MONITORING IN IoT
USING BIG DATA ANALYTICS
In general, big data refers to the collection of a large volume of structured
and unstructured data such as unstructured medical notes, medical imaging
data, genetic data, DNA sequences and the patient’s behavioral data received
from the sensors. Big data is used when large volumes of data cannot be pro-
cessed by using traditional methods. The large volume of data is deployed
in the cloud, which can be accessed anytime using the Internet (Viceconti
et al. 2015). The definition of big data consists of three Vs: (1) Volume: The
data can be collected from various sources, leading to storage insufficiency.
By using a cloud platform like deep lakes and Hadoop, the burden of storage
can be reduced. (2) Velocity: Due to the large accumulation of data, the speed
of data is reduced. By using RFID tags and sensors, the speed of data transfer
is increased in real time. (3) Variety: Data can be in different forms such as
structured and unstructured formats. The main objective of the system is to
collect data from the sensor and analyze the same using the Hadoop platform.
The system gathers the health information of the patient using the sensors, the
data are processed by microcontrollers and communication is done wirelessly
to the other devices in the system. The sensor-captured data are analyzed and
stored in the cloud by a healthcare proxy. The IoT agent—Intel Galileo Gen
2—is used as a healthcare proxy (Kumar et al. 2018). A graphical user inter-
face is used to monitor the patient’s health. The proposed architecture consists
of a blood pressure sensor, a heart rate sensor and a humidity sensor, which are
connected with the health proxy. Health parameters such as temperature and
blood pressure are stored in the cloud.
Data collected from the different healthcare institutions are accumulated as mas-
sive amounts of data that are warehoused in the cloud technology (Zhou et al. 2020).
Since the data are stored in the cloud, the past examination of the health status of a
specic patient is analyzed. The smartphone is directly connected to the IoT agent
7 Intelligent Healthcare Systems
through general-purpose input/output pins; therefore, the doctor’s mobile phone is
used to collect the health data of the patient (Dhayne et al. 2019). If there is an emer-
gency situation, proper medical instructions should be provided. There is connec-
tivity from the sensors to the cloud and from cloud to the end users. Data analytics
are executed by Hadoop Distributed File System and MapReduce process. Hadoop
is an open-source software used to solve problems with a massive amount of data
and computation. It uses the MapReduce programming framework for processing
big data with a better fault tolerance. The sensors are to be touched by the patient to
test the electrophysiological gestures of the patient. The readings from the sensors
are observed using the Arduino processor. From the sensors, the IoT agent is con-
nected to store the data. The GSM module is provided with voice, SMS, fax and data
application for doctors. When sensor values like heartbeat rate are noted, they are
recorded by the IoT agent, and they are transferred to the ThingSpeak cloud platform
(Sethuraman et al. 2020).
The sensor data can be accessed from the cloud with the help of the Hadoop
platform using the query command Hive. The MapReduce process is used to
process the data from the cloud. A mobile phone is connected to the IoT agent
with surface-mount technology package and an unlocked mini subscriber iden-
tity module. Based on the threshold value, the alert system is created. Once the
emergency is aroused, an alert message is passed on immediately (Kim et al.
2020). When the threshold value exceeds, it sends an alert message to the doc-
tors or healthcare providers. In real time, big data analysis of health information
is carried out. Due to big data analytics, the response time of the system is less.
By periodical monitoring of health parameters, the alert messages are transferred
by means of mobile phones using GPRS or GPS connection. It offers the doctors
a huge amount of health data and provides the right medical instructions to the
right person at right time. The advantages of big data include data optimization,
low cost, data management, the highest level of security and biomedical research,
among others (Zhang et al. 2015).
1.4 INDUSTRIAL IoT-ENABLED FRAMEWORK
FOR HEALTH USING THE CLOUD
Due to the increased number of geriatric patients, it is more important to monitor
health parameters such as blood pressure, heartbeat rate, oxygen rate and the like in
a periodical manner. The Health Industrial IoT (IIoT) is the amalgamation of sensors
and devices, communication devices and mobile applications. Health IIoT is used
to track, monitor and accumulate data. In this framework, the health parameters
are collected by communicative devices like mobile phones and sensors and the
data is shared securely to the cloud; then they can be accessed by the professionals.
Healthcare IIoT is a combination of large number of interconnected devices and
cloud computing devices (Kumar et al. 2020). Cloud technology is used to collect
patients’ data in an endless manner. The IIoT is the mixture of big data analytics,
Internet-connected sensor devices, machine-to-machine communication, the cloud
and the real-time examination of data collected from the sensors. Through the IIoT,
a large amount of data can be accessed anywhere anytime.
8 Knowledge Modelling and Big Data Analytics
Data collected from the interconnected devices and applications are gathered
and analyzed. The gathered data are analyzed from medical records like electro-
cardiograms (ECGs), medical imaging equipment and physiological sensors. The
collected data are further processed and classied to help doctors make decisions.
The data are transferred in an endless manner through gateways to the data cen-
ters. In a cloud-based remote health monitoring system, heartbeat signals are mon-
itored and recorded through sensor devices and are stored in remote cloud storage.
After monitoring, the signal features are extracted and enhanced; furthermore, it
is stored in the cloud. The information stored is validated by the cloud to check
whether the entered information is correct. Once the features are extracted and
validated, the data get redirected to healthcare experts or professionals (Takahashi
et al. 2017).
The health monitoring system (Yang et al. 2018) consists of signal enhancement,
extraction of features, analysis of the ECG, reconstruction of signal and watermark-
ing, which are done on the end user. In this system, the ECG signal is produced by
the medical equipment, which is recorded by mobile phones and stored in the cloud.
Physiological rarity can be a source for muscular activities that result in small skews
that causes large oscillations in the collected data. A non-physiological rarity can
be caused by electrical intrusion and electrode faults. Electrode faults are caused
by misplacing electrodes, having loose connections, not using enough electrode gel,
setting a wrong lter, damaged wires and so on. The major error in ECG data accu-
mulation is caused by misplacing electrodes and displacing cables, which result in
cardiac abnormalities like an ectopic rhythm. In the stage of enhancement, the ECG
signal is recorded and sent to a low-pass lter. The low-pass lter lters the high-
frequency components in the signal. The output of the low-pass lter is passed to
the 25-point moving average lter to smooth the signal. By passing the ECG signal
through these two lters, the signal is free from high-frequency components and
looks clear. This preprocessing step is much needed to nd the electrical signals in
the ECG. An average wavelet transform is used to detect the peak R value. An aver-
age wavelet transform is a combination of a Hilbert transform and a wavelet trans-
form. These transforms combine the time-frequency location and the local slope
transformation obtained from the Hilbert and wavelet transforms. The next step in
ECG monitoring is to protect the data from forgery. Forgery can be prevented by
watermarking the ECG signal.
Watermarking refers to inserting some information without altering the informa-
tion context. There are two types of watermarking, namely, (1) watermark embedding
– in this process, the authenticity of the ECG signal is transferred to the cloud – and
(2) watermark extraction –it extracts the features of ECG signal for data veri-
cation. Watermarking is done on the basis of discrete wavelet transform–singular
value decomposition (DWT-SVD). DWT decomposes the signal into multiple levels
of time and frequency. SVD is a technique for matrix factorization that decays the
matrix into three matrices. From the ECG signal, several features like heartbeat rate,
P wave duration, PR interval, T-shape wave and QRS complex are extracted. To map
the feature space into higher dimensional space, a one-class support vector machine
classication is used. It is the mechanism that trains only one class. It checks whether
the detected ECG signal is normal or not.
9 Intelligent Healthcare Systems
1.5 PATIENT HEALTH MONITORING SYSTEM BASED
ON THE IoT
In this system, wireless sensing node technology is used to detect the unforeseen
circumstances of health. Nowadays, people are facing serious health issues that may
lead to their sudden demise. To avoid this, a special monitoring device has been
developed to monitor patients and communicate with their neighbors. The health
monitoring system is made up of wireless sensors like temperature sensors and
heartbeat sensors to track the patient’s temperature and heartbeat rate. It makes use
of sensor technology and informs their sensor neighbors about the patient’s health
status when they are in critical condition. Both the sensor and the system are con-
nected to an Arduino Uno processor (Chaudhury et al. 2017).
A microcontroller is connected with the LCD display and Wi-Fi module to dis-
patch the data to the wireless sensing node. If there is any sudden change in the
patient’s heartbeat or temperature, an alert message is conveyed to the neighbor using
the IoT. It monitors the patient with a time interval through the Internet. Generally,
the IoT-based healthcare system consists of two topologies:(1) IoT net topology, an
amalgamation of physical devices like laptops, smartphones and so on and applica-
tion-oriented devices, and (2) IoT net architecture, which is dened as the speci-
cation of physical devices organization and their practical association. The sensor
system consists of self-organized devices that utilize sensors to track physiological
factors. The sensor network gathers the data from the network and passes the data
to clients using the Internet. The architecture consists of an Arduino Uno processor
with an Atmega controller. The Atmega controller is interfaced with different equip-
ment. The microcontroller collects the data from the sensor and processes the data
(Satapathy et al. 2020). A Wi-Fi module is connected to the microcontroller, which
shares the sensor data through the network. LM 35 is a body temperature sensor that
measures the temperature in the Celsius scale. The temperature measured is directly
proportional to the output signal. It operates at 4V and draws a current less than
60 microamps. It can operate at an optimal temperature and provides good accuracy.
The conversion from the output voltage to the centigrade scale is very easy. The
LM 35 is attached with the microcontroller. The measured temperature is uploaded
to the Internet through the IoT. If the threshold value is exceeded, an alert will be
given to the nearby sensors. The LM 358 heartbeat sensor is used to measure the
ECG signal, that is, heartbeat rate. The heartbeat sensor is attached to the microcon-
troller. The measured values from the sensors are sent to the microcontroller through
the Internet. If the sensor value crosses the threshold value, an alert notication is
shared. The LCD display is interfaced and will display the message (Bi et al. 2019).
The Wi-Fi module transfers the message by means of the Internet. Thus, patient
monitoring is effectively done by the Internet.
1.6 IoT SMART HEALTH MONITORING SYSTEM
The abnormal conditions of the patients can be possible with the use of remote
healthcare systems. The instant solution can be provided by wearable smart
devices, which screen the health activities and evaluate the patient on day-to-day
10 Knowledge Modelling and Big Data Analytics
basis. The sensor data are captured and analyzed by the devices and provides a
response to patients. In this system, a diverse range of healthcare metrics are con-
sidered and measured using smart devices. To collect all the mentioned param-
eters, the device should be coupled with the mobile devices. A smart watch is one
among the reliable wearable devices that continuously monitor the body looking
for the accurate heart rate and photoplethysmography (PPG) signal. It is a highly
effective and low-cost device that prevents unsafe activities in human body. It
combines various sensor types, such as bio-impedance sensor, accelerometer,
temperature, heart rate, capacitive and motion sensor. Accelerometer values are
used to predict the mobility of a patient by using ECG sensor to monitor the
patient’s heart rate, and the oxygen level is measured using SpO2 (Dhayne et al.
2019; Zhang et al. 2015).
A temperature sensor is used to monitor body temperature, and these data are
considered to be one of the vitals. A bio-impedance sensor is used to collect the
information about cell pathology and physiology. A capacitive sensor is used in
touch screen displays. Low-energy Bluetooth is used in smartwatches to communi-
cate with other low-energy devices like mobile phones, Raspberry Pi 3 and others.
It is used because of its reduced power consumption. Near-eld communication
protocol provides communication between the two Bluetooth devices. A micro-
controller obtains signals from the sensors through an analog-to-digital convertor.
The wireless connection through the Bluetooth device is established by a universal
asynchronous receiver transmitter (UART). The signal from the microcontroller is
searched by an antenna through the UART. The standby time of the watch is man-
datory option. In order to improve the standby time, the display is removed. The
display is connected to the smartphone, through which the battery level and appli-
cations can be accessed. The smartphone is an Android device with a touchscreen,
cellular network and an operating system. To transfer the data from the smartwatch
to cloud, the smartphone is used. It acts as a bridge between the wearable device
and the smartphone. An Android application has been created to develop a com-
munication between the smartwatch and the server (Yang et al. 2018; Chaudhury
et al. 2017).
The data are collected in real time by the smartphone in JSON format. The JSON
format is further converted into the form of charts. The application is attached to
ThingSpeak API for initiating the smartwatch and ThingSpeak cloud for communi-
cating the data. Finally, the data are sent to the cloud. ThingSpeak technology is used
to access the data through smart devices that are worn on the human body and that
continuously monitor human health. The ThingSpeak cloud is a MATLAB® analyt-
ics cloud where the data can be accessed and processed under different conditions. In
a cloud platform, the heart parameters are analyzed whether the cardio condition is
normal or not. The data are analyzed to check the patient’s heart health. HTTP pro-
tocol is used to forward the alert messages to the neighbors of the patients and to the
nearby doctors. The server-to-server communication is carried out by a web-based
service provider called IFTTT (If This Then That). Applets are simple conditional
statements created by IFTTT. When there is a trigger, the applets pop up. It works
when there is an emergency; the alert is initiated on the smartphone. The emergency
alert is forwarded to a relative of the patient and the doctor (Bi et al. 2019).
11 Intelligent Healthcare Systems
1.7 IoT-BASED HEALTHCARE MONITORING SYSTEM
IoT emerged from the eld of information and communication technologies. It allows
the devices to transmit and receive data through an Internet methodology. The IoT
plays an important role in the health industry, which includes monitoring of elderly
and disabled people (Kumar et al. 2018). In the eld of wireless communication,
patient monitoring for a health condition is carried out continuously. The monitored
data are transferred to the network through a Wi-Fi module. In the case of any emer-
gency or if a critical condition occurs, a notication is sent to the patient’s caretaker
or doctor. For an efcient monitoring system, cloud computing is used to protect the
data and maintain security measures. Wi-Fi modules provide authorization, security
and privacy. They allow access to the veried users.
The main concept behind the system is to monitor the patient continuously
throughout the interval. It is a three-tier architecture framework that includes a
Wireless Body Sensor Network (WBSN), a base station, and a graphical user inter-
face. The WBSN is composed of wearable sensors like ECG sensors, temperature
sensors and heartbeat sensors (Sethuraman et al. 2020; Kim et al. 2020). The WBSN
acts as a data-gathering unit and collects the physiological signals from the sen-
sors that are attached to the patient’s body. The gathered data are passed onto the
base station or the server through the Wi-Fi module. The data forwarded from the
network are stored in the form of les that can be used in the future. From the base
station, the required data can be accessed anytime anywhere by typing in the autho-
rized IP address at the user device. A graphical user interface is used to save and
analyze the data in graphical and text formats.
The graphical user interface is used to share the alert message to the health
experts through the Global System for Mobile Communication (GSM). The IoT
architecture uses an Arduino Uno processor with an Atmega 328 p microcontroller,
an LCD, a buzzer and a Wi-Fi module. The alert sound is given through buzzer. The
health status of the patient is displayed by the LCD. HTTP protocol provides com-
munication between the Wi-Fi and the web server. The webpage gets refreshed for
every 15 seconds. The remote server uses FTP protocol to send emails and SMS. The
temperature sensor used here is LM35. These types of sensors are very accurate and
have high precision. The output voltage increases linearly with the Celsius scale. It
doesn’t undergo oxidation process. It is easy to convert the output voltage into tem-
perature. ECG sensors consist of electrodes that are placed on the patient body to
monitor ECG signals. The ECG sensor is connected to the AD8232 single-lead heart
rate monitor, and it acts as an operational amplier or op amp. It reduces the noise
in the ECG signal. It gives PR and QT signals as output. A heartbeat sensor is used
to measure the oxygen level in the hemoglobin, and it also measures the heartbeat
per minute. It ensures the blood volume is similar to the heartbeat. Arduino Uno
is the microcontroller board. It consists of six analog inputs, 14 digital input and
output pins, a 16-MHz quartz oscillator, a USB connector, a power supply jack, an
ICSP header and a reset button. A microcontroller can be connected to the computer
using the USB connector. The microcontroller has its own memory unit and proces-
sor (Satapathy et al. 2020; Bi et al. 2019). The microcontroller is used to control the
information. Raspberry pi is a small computer that is used to run the codes. It has a
12 Knowledge Modelling and Big Data Analytics
Linux operating system. Arduino is used as an interface between the operating sys-
tem and Raspberry pi. When the Arduino Uno is connected to the Wi-Fi module, the
Wi-Fi module acts as a modem and Raspberry pi acts as a server. In order to transmit
the data, HTTP protocol is used. The buzzer is made up of a piezoelectric crystal
that produces sound. Whenever an abnormality is detected, the buzzer produces the
sound to alert caretakers and doctors.
1.8 MONITORING OF HUMAN BODY SIGNAL BASED
ON LoRa WIRELESS NETWORK SYSTEM
The LoRa wireless network consists of a temperature sensor, an ECG sensor, an
oxygen pulse rate sensor and an oxygen saturation sensor, along with MySignals
(Chushig-Muzo et al. 2020). These sensors are enabled by MySignal to collect data.
The main aim is to transfer the data from MySignals to a computer using a LoRa
network. The sensors are interfaced with the MySignals platform. Generally, a LoRa
network is dened as a long-range network that works on the upper layer of the net-
work. It is based on cloud access of the network layer. It can connect devices up to
50 miles apart. It requires only minimal power for its operation. It provides end-to-end
encryption and high security with authentication. Sensors connected in the LoRa
network are known as picocells. LoRa is based on spread spectrum modulation.
LoRa has unique features that are different from other short-range technologies (Din
et al. 2019). LoRa uses mesh networking technology. LoRa can be used in medical
eld to monitor the patient by transmitting the data through sensors. The collected
data are sent to the cloud for further processing. The cloud has high communication
range with low data rate. LoRa technology has wide applications in various elds,
such as agriculture monitoring, air pollution monitoring, home security, eet man-
agement, indoor air quality, animal processing, industrial temperature monitoring
and others. Physiological sensors like temperature sensors, ECG sensors and heart
rate sensors are connected to the MySignals hub.
From MySignals, the Arduino Uno is connected to collect the data with wired
and wireless connections like Bluetooth and others. The LoRa protocol connects
the Arduino and MySignals platform by a radio shield. The communication and
transmission of data are carried out through wire, Bluetooth, Wi-Fi or LoRa. The
MySignals platform consists of 12 sensor ports. The platform is interfaced with Wi-Fi
serial transceiver module ESP8266 (Misran et al. 2019). The data obtained from the
sensors are transferred through the LoRa module and from the Waspmote gateway to
the computer. LoRa is coupled with a multiprotocol radio shield, which can connect
to multiple devices at the same time. The multiprotocol radio shield can be coupled
with any short-range wireless modules like Bluetooth, Wi-Fi, Zigbee, RFID and so
on. The LoRa protocol is connected to the Waspmote gateway. Waspmote is an ultra-
low-power open-source wireless sensor platform. It allows the sensor nodes with a
low-power consumption mode to operate autonomously. RealTerm is a hyper termi-
nal of Waspmote that is used to capture, control and debug the binary and other data
formats. RealTerm is used to connect LoRa modules to send and receive the sensor
data with the specic baud rate for communication. It can operate at the frequency
of 868 MHz or 900 MHz.
13 Intelligent Healthcare Systems
The physiological signals are collected from the sensors and were selected to
predict the health condition of the patient and check the reliability of the data. The
sensor data can be assessed by two methods. The sensor data are collected by serial
monitor through Bluetooth, and RealTerm collects the data from the Arudino Uno
and LoRa modules. The gateway receives the messages from the LoRa module node
for a particular period. LoRa operates at a frequency of 900 MHz ISM band with a
data rate of 12.5 kbps. The data collected from the sensors are transmitted at the date
rate of 8–10 kbps to the cloud. LoRa has an acceptable baud rate of 115200 bps. For a
proper transmission, the baud rate should be similar for both transmission and recep-
tion. The duty cycle of LoRa is less than 1%. When one or more devices transmit
their data packets at the same time, there may be a loss of data packets or network
overload. In a LoRa module, the data packets are sent properly if the device is con-
gured at different times to transfer. The microcontroller unit, the sensor unit and
the LoRa unit consume more energy (Tayeh et al. 2020). The transmission power of
the Waspmote is 14dBm. The MySignals unit is operated by battery power and con-
sumes low power. Due to increased distance, the transmission and receiving power
is high, which, in turn, reduces the transmission rate. Secure communication is an
important factor at IoT solutions. The data transmission should be reliable, and it
should be free from vulnerabilities. In an IoT protocol, each layer is vulnerable to
security issues. To maintain the security in the network, LoRa provides end-to-end
AES-128 encryption. The application key encrypts all the data with optimal privacy
and protection. AES-128 is known as an application key. The App key produces two
keys, namely, the Network Session key (NwKSkey) and the Application Session key
(AppSkey). The NwKSkey creates a specic signature to ensure message integrity
for each and every device. Every application-specic session key is responsible for
the encryption and decryption of the application data (Valach et al. 2020).
1.9 A FRAMEWORK FOR HEALTHCARE SYSTEM
USING CLOUD COMPUTING
The healthcare industry is one of the major industries growing toward development.
In the eld of medicine, information technology plays a major role. Even though
there is growth in the health industry, some organizations are still based on hand-
written prescriptions and medical records. These handwritten notes may be lost or
damaged. In order to maintain medical records, digital information is used. The
patient’s information is stored on the Internet and can be accessed when needed. In
the digital world, sharing data is more complex and rarer because it may be subject
to many kinds of attacks. It has a capacity to connect device to machine, object to
object, patient to doctor, patient to machine, sensor to mobile, alerting (smart devices
to human), and RF reader. It ensures the effectiveness of the health industry by con-
necting devices, machines and humans smartly (Al Brashdi et al. 2018; Dineshkumar
et al. 2016; Hossain et al. 2016).
The biggest challenges in an IoT-based health industry are security, tracking and
monitoring the patients. The cloud IoT consists of different applications like e-medical
prescription systems, personal health records, clinical decision-making systems,
electronic health records and pharmacy systems. The doctor can use the cloud IoT
14 Knowledge Modelling and Big Data Analytics
for the improvisation of clinical records and diagnosis of patients. Self-assessment
can be done by the patient about their health condition. They can also nd hospitals
and clinical services. Physical activity can be monitored by a personal monitoring
system. If a person is wearing a monitoring device, they can monitor their physical
activities and sleep activities data. These devices can be placed on the human body.
It can be used in the shape of tiny devices that can be embedded into the jewelry,
under the skin, or under the clothes. Thus, the monitoring devices are embedded into
the body, which creates a WBSN. These networks are capable of collecting samples,
processing samples and communicating with each other. It helps nd the person’s
location and differentiate the person’s state like resting, walking, sleeping and the
like.
The data collected are updated to the device through an electronic health records
system. The data are shared with the cloud application and get updated to the patient’s
medical prole. The updated prole can be shared to doctors and hospital. Patient
records consist of X-rays, computed tomography (CT) scans, and magnetic reso-
nance imaging (MRI) scans that can be uploaded in real time and shared through
cloud platforms. These updates can be accessed by any health expert, such as a car-
diologist or a radiologist, on demand of patient. When someone is traveling abroad,
they could get a health checkup from local doctors on the ground with immediate
access of their health records. The prescription provided by the doctor is available
to the associated pharmacist. The health records can be accessed anywhere anytime
through network technologies. The advantage of this system enables low-cost tech-
nology and speedy recovery of the patient.
1.10 WEARABLE DEVICES
Wearable devices are also known as smart devices that can be placed or worn by
the patient for their health monitoring. Wearable devices are used to monitor heart
rate, blood pressure levels, the distance walked and so on. Day by day, health issues
and pandemic issues are arising that need continuous monitoring. Continuous moni-
toring can be done by smart devices like a smartwatch, a tness tracker and the
like. The devices collect the data in real time and shared through some kind of
communication. Personalized health information like physiological signals can be
collected from the sensors attached to the human body and can be accumulated
by Android-based mobile data acquisition. The data accumulated can be accessed
by the smartphone and transferred to the server for future processing. For com-
munication between the devices, Bluetooth with low energy is used. A Bluetooth
Low Energy (BLE) device increases the efciency of the device. The architecture of
wearable devices is made up of a sensor network, BLE, an Android application and
a server.
The sensor network consists of tiny sensors that can be embedded or implanted
in the human body. These sensors collect data in real time and pass the information
to a mobile device through BLE. From the mobile application, the data is passed to
the server. From the server, the data can be accessed. BLE is a power-consumption
technology when compared to Bluetooth. For continuous monitoring, the energy is
maintained by BLE. It operates at a frequency of 2.4 GHz ISM band. BLE remains
15 Intelligent Healthcare Systems
idle until the connection is established. For a short connection, the data rate is
1 Mb/s. To enable BLE in Android applications, mobile phones are used. For this net-
work, Android 4.3 is used to enable BLE. To use BLE on the receiver side, the
following steps are used: A Generic Attribute (GATT) Prole is used to send the
data over the BLE link. More than one prole can be implemented in the device.
An Attribute (ATT) Prole is placed at the top of the GATT Prole. The attributes
are converted into characteristics and services. Characteristics mean single values.
Descriptors give different values to the characteristics. Services are dened as a
collection of characteristics. The features of Bluetooth can be used by the Bluetooth
permission BLUETOOTH. The permission is required to perform the request and
accept the connection. The device needs to support both peripheral and central to
establish the connection. The transfer of GATT metadata is started once the connec-
tion is established. Bluetooth permission requires starting, accepting and transfer-
ring the data. The BLE device is nding by scanning the nearby devices. To interface
with the BLE device, a GATT server connection is established. The GATT server
is connected to the Android app, which is used to discover the services. Once the
data transfer nishes, the connection gets closed. The data, updated to the mobile
application, are transferred to the server. While the data are on the server, they can
be accessed by anyone with valid credentials like a username and password. Once
the valid credentials are entered, the data can be accessed. The application screen
displays the test results obtained from the BLE transmitter.
1.11 PRECISION MEDICINE AND HEALTH STATE
MONITORING THROUGH WEARABLE DEVICES
Precision medicine main focuses on the tailored treatment for individual-centric
characteristics. Precision medicine involves large datasets for discovering the dis-
ease mechanisms. A key principle of using the deployment of precision wearable
devices inuences the seven physiological signals such as heart rate, respiration rate,
temperature, blood pressure, physical activity, sweat and emotion (Tembhare et al.
2019; Botero-Valencia et al. 2018; Polonelli et al. 2019).
1. Measurement of Heart Rate: The heart is the main organ of the human
body that pumps and regulates blood circulation throughout the body. It
puries the blood and supplies enough nutrient content to the body. The
heart pumping rate is measured from the physical activity carried out by
the body. For an athlete, heart rate varies from 40–60 bpm, and for adults
and children, heart rate varies from 60–100 bpm. In an ECG, the electrical
activity of the heart can be tracked by placing electrodes on the chest. The
pulse rate and the acoustic signal associated with heartbeat can be tracked
by stethoscope. The heart rate can be obtained manually by listening the
mechanical pulses of the artery. The contraction and enlargement of vessels
make changes in optical transmission or reection which can be observed
by PPG (Tembhare et al. 2019).
PPG: It is used to ration the variations in the volume of organ or tissue. It
uses an optical light source and detector to measure the volume of the organ
16 Knowledge Modelling and Big Data Analytics
or tissue. The enlargement and reduction of blood vessels in the dermis
and the hypodermis depend on the changes in the blood volume. The PPG
signal measures blood oxygen saturation level, stroke volume, heart rate,
and vascular resistance. PPG has a wavelength between 700–1000 nm. PPG
sensors are embedded in wrist-worn devices with wireless communication
support. The photo detector emits green, red or infrared LEDs. Wrist-worn
devices are incorporated by battery and transmission electronics. PPG sen-
sors are portable and convenient. These sensors can be worn on the ear
lobe or wrist to measure the heart rate. The major disadvantage of this sen-
sor is that measurement at ear lobe decreases the temperature results in a
decrease of the signal-to-noise ratio.
Impedance Cardiography (ICG): This device processes variations in the
complete electrical conductivity of the thorax. It is used to evaluate the
cardio-dynamic bounds like stroke volume, vascular resistance and heart
rate. It operates at the frequency of 20–100 kHz, with sinusoidal perturba-
tion of 1–5 mA. These devices can be placed on the user’s chest, waist or
neck. The disadvantage of ICG is that it could not produce accurate results.
2. Measurement of Respiration Rate: The respiratory system in the human
body includes cavities, the nose, the mouth, the bronchioles, the lungs and
the muscles responsible for respiration, among others. Respiration rate dif-
fers from 30–60 breaths per minute for newborns and 12–18 breaths per
minute for adults. Due to alcohol consumption and sleep apnea, respiration
rate may be less than eight breaths per minute. While exercising, the respi-
ration rate increases, which, in turn, increases oxygen intake. To measure
the lungs’ volume, capacity and gas exchange are measured using a pulmo-
nary function test (PFT). In a PFT, the single volume breath is measured
during inhalation and exhalation. The efciency of oxygen exchange in vas-
cular system is measured by lung diffusion capacity (Botero-Valencia et al.
2018; Polonelli et al. 2019).
Spirometry: Wearable devices are used to measure the oxygen ow rate
and volume during inhalation and exhalation. An ergo-spirometry mask
is the most common wearable spirometer device. An ergo-spirometer is
used to measure the speed and amount of air inhaled and exhaled dur-
ing exercise. A spirometer uses an ultrasonic transducer to measure the
speed of airow, and the mask is used to nd the pressure difference.
These are smaller and lighter in size. Spirometers are mostly used in a
clinic.
Respiratory Inductance Plethysmography (RIP): It detects the enlarge-
ment and shrinking of the chest and abdomen. It consists of two bands;
each band consists of a wire loop. One is kept on the chest, and the
other one is on the abdomen. The loop is zigzagged to change its length.
During enlargement and reduction, there is a change in the magnetic eld
prompted in the loop. For continuous monitoring of respiration rate, RIP
is used. These devices are light in weight and suitable for kids and infants
(Ayaz 2018).
17 Intelligent Healthcare Systems
3. Measurement of Blood Pressure: For normal heart function, the pressure
of the blood varies from maximum systolic to minimum diastolic. The most
common three methods to measure blood pressure are palpitation, listening
to Korotkoff sounds using a stethoscope and the oscillometric method. An
increase in stress, drugs and irregular physical activity could lead to blood
pressure in a high volume. The normal blood pressure value varies from
120 mm HG in the case of systolic and less than 80 mm Hg in the case of
diastolic. The systolic pressure varies between 160–220 mm Hg, and dia-
stolic pressure varies less than 100mm Hg while exercising (Polonelli et al.
2019; Cheol Jeong et al. 2018).
Oscillometric Blood Pressure: Manual blood pressure measurement
is carried out by using palpitation or a stethoscope to listen to Korotkoff
sounds. The pressure uctuations in the cuff can be measured by the oscil-
lometric method. Since arterial elasticity depends on pressure, the magni-
tude of oscillations changes with pressure. Wearable oscillometric devices
include an air bladder cuff, pump, valves, pressure sensors, a power sup-
ply, display and communication devices for wireless transmission of data
(Bigelow et al. 2016). These are wrist and ngertip devices. These devices
are used for their high accuracy and robustness. The systolic and diastolic
pressure can be measured from the maximum amplitude.
4. Measurement of Temperature: Generally, body temperature is measured
manually by using a thermometer. The normal temperature of the human
body is 98.63°F. For healthy adults, the body temperature varies from
97.8°F to 99°F. A glass thermometer is used to measure the temperature
where the core temperature is displayed on the LCD. Most thermometers
are thermistor-based, which are more sensitive. These devices are placed
on the wrist, forehead, chest, ears, feet, ngers and other parts of the body.
To measure the temperature at the tympanic membrane, an infrared ther-
mopile sensor is used. Surface temperature is based on the exchange of heat
at the environment. Athletic performance, fatigue, emotional condition and
sleep states are monitored by surface temperature (Bae et al. 2017).
5. Measurement of Physical Activity: Physical activity is the movement of
body produced by the skeletal system that requires energy. Physical activ-
ity is important for individuals with cardiovascular diseases, diabetes and
obesity. Fitness trackers and wearable accelerometers are used to monitor
the daily activity of the patient. A wearable accelerometer measures the
acceleration with one or more axes. A gyroscope measures angular motion.
An accelerometer is small in size, and it can be wearable (Cheol Jeong
et a l. 2018).
1.12 CONCLUSION
In this chapter, an IoT-based healthcare system was analyzed along with the dif-
ferent applications. This chapter also addressed the real-time usage of wearable
devices. From observations, it is clear that an IoT-based healthcare system reaches
18 Knowledge Modelling and Big Data Analytics
different dimensions of the information technology eld. The usage of IoT in medi-
cal standards makes the system to be cost-effective, and it provides speedy recov-
ery of the patient. Furthermore, it has also been proved that wearable devices also
provide effective and efcient monitoring. With the help of these devices, health
parameters are monitored, analyzed and transferred to the cloud or server. From
the cloud or server, the data can be accessed anywhere by healthcare experts. This
makes the entire healthcare services feasible. In the future, there may be a chance of
an autonomous healthcare service.
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Zhou, S. , He, J. , Yang, H. , Chen, D. , & Zhang, R. (2020). Big Data-Driven Abnormal Behavior
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