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A novel group decision making model based on neutrosophic sets for heart disease diagnosis

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In a developed society, people have more concerned about their health. Thus, improvement of medical field application has been one of the greatest active study areas. Medical statistics show that heart disease is the main reason for morbidity and death in the world. The physician’s job is difficult because of having too many factors to analyze in the diagnosis of heart disease. Besides, data and information gained by the physician for diagnosis are often partial and immersed. Recently, health care applications with the Internet of Things (IoT) have offered different dimensions and other online services. These applications have provided a new platform for millions of people to receive benefits from the regular health tips to live a healthy life. In this paper, we propose a novel framework based on computer supported diagnosis and IoT to detect and monitor heart failure infected patients, where the data are attained from various other sources. The proposed healthcare system aims at obtaining better precision of diagnosis with ambiguous information. We suggest neutrosophic multi criteria decision making (NMCDM) technique to aid patient and physician to know if patient is suffering from heart failure. Furthermore, through dealing with the uncertainty of imprecision and vagueness resulted from the symmetrical priority scales of different symptoms of disease, users know what extent the disease is dangerous in their body. The proposed model is validated by numerical examples on real case studies. The experimental results indicate that the proposed system provides a viable solution that can work at wide range, a new platform to millions of people getting benefit over the decreasing of mortality and cost of clinical treatment related to heart failure.
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A novel group decision making model based
on neutrosophic sets for heart disease diagnosis
Mohamed Abdel-Basset
1
&Abduallah Gamal
1
&Gunasekaran Manogaran
2
&
Le Hoang Son
3
&Hoang Viet Long
4,5
Received: 28 February 2019 /Revised: 20 March 2019 /Accepted: 9 May 2019
#Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract
In a developed society, people have more concerned about their health. Thus, improvement of
medical field application has been one of the greatest active study areas. Medical statistics
show that heart disease is the main reason for morbidity and death in the world. The
physicians job is difficult because of having too many factors to analyze in the diagnosis of
heart disease. Besides, data and information gained by the physician for diagnosis are often
partial and immersed. Recently, health care applications with the Internet of Things (IoT) have
offered different dimensions and other online services. These applications have provided a new
platform for millions of people to receive benefits from the regular health tips to live a healthy
life. In this paper, we propose a novel framework based on computer supported diagnosis and
IoT to detect and monitor heart failure infected patients, where the data are attained from
various other sources. The proposed healthcare system aims at obtaining better precision of
diagnosis with ambiguous information. We suggest neutrosophic multi criteria decision
making (NMCDM) technique to aid patient and physician to know if patient is suffering from
heart failure. Furthermore, through dealing with the uncertainty of imprecision and vagueness
resulted from the symmetrical priority scales of different symptoms of disease, users know
what extent the disease is dangerous in their body. The proposed model is validated by
numerical examples on real case studies. The experimental results indicate that the proposed
system provides a viable solution that can work at wide range, a new platform to millions of
people getting benefit over the decreasing of mortality and cost of clinical treatment related to
heart failure.
Keywords Heart failure .Internet of things (IoT) .Neutrosophic multi criteria decisionmaking .
Biomedical data analysis
https://doi.org/10.1007/s11042-019-07742-7
*Mohamed Abdel-Basset
analyst_mohamed@zu.edu.eg
Extended author information available on the last page of the article
Multimedia Tools and Applications (2020) 79:997710002
Published online: 28 May 2019
/
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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