Health Tourism with Data Mining: Present State and Future Potentials

Abstract

Health Tourism is a trendy interest area as it allows individuals to travel another country for receiving health services while experiencing touristic opportunities. There are many benefits of health tourism in terms of costs, services, and experiences. By combining both health and tourism area, it welcomes many research directions in not only healthcare and tourism but also marketing and business management. Among these the area of Health Tourism is intensively connected with digital solutions since it is important to reach out individuals in different countries and enabling them to decide and come to the host country for receiving services. So, the data is very important for establishing innovative Health Tourism applications. However, one question arising is about how to analyze, process and use the data effectively within the purposes of such applications. This paper tries to answer this question from the perspective of Data Mining. As the Data Mining allows creating descriptive and predictive new knowledge from the known data, its use in digital Health Tourism applications has a remarkable value. In this context, this paper firstly ensures the connection between Health Tourism and Data Mining, and then discusses about present state as well as the future. It is believed that the outcomes from this paper will be a triggering factor for further research in Data Mining, which is currently a niche application way in Health Tourism.

Full text

International Journal of Information Communication Technology and Digital Convergence
Vol. 8, No. 1, June 2023, pp. 23-33
ISSN: 2466-0094
Copyright ⓒ IJICTDC
Health Tourism with Data Mining: Present State and Future Potentials
G. Kose *
Aydin Adnan Menderes University, Graduate School of Health Sciences, Turkey
Email: gamze.g.kose@gmail.com
O. E. Colakoglu
Aydin Adnan Menderes University, Faculty of Tourism, Turkey
Email: oecolakoglu@gmail.com
* Corresponding author
Abstract
Health Tourism is a trendy interest area as it allows individuals to travel another country for
receiving health services while experiencing touristic opportunities. There are many benefits of
health tourism in terms of costs, services, and experiences. By combining both health and tourism
area, it welcomes many research directions in not only healthcare and tourism but also marketing
and business management. Among these the area of Health Tourism is intensively connected with
digital solutions since it is important to reach out individuals in different countries and enabling
them to decide and come to the host country for receiving services. So, the data is very important
for establishing innovative Health Tourism applications. However, one question arising is about
how to analyze, process and use the data effectively within the purposes of such applications. This
paper tries to answer this question from the perspective of Data Mining. As the Data Mining
allows creating descriptive and predictive new knowledge from the known data, its use in digital
Health Tourism applications has a remarkable value. In this context, this paper firstly ensures the
connection between Health Tourism and Data Mining, and then discusses about present state as
well as the future. It is believed that the outcomes from this paper will be a triggering factor for
further research in Data Mining, which is currently a niche application way in Health Tourism.
Keywords: health tourism, data mining, healthcare, tourism, technology, future
1. Introduction
Health Tourism has been a rapidly growing tourism way in especially last decade.
From a general perspective, Health Tourism aims to take individuals to another country
where they can receive health services such as medical operations, specific treatments,
and physical or mental well-being activities [1-3]. These individuals are somehow special
tourists, who are able to keep themselves healthy while traveling and seeing. According to
the reports and findings, Health Tourism actually backs to even old Greek times [4]. But it
has been evolved in time and started to take place again in showcase, as a result of
globalism and increasing opportunities to hear and see about anywhere around the world.
The report by the World Tourism Organization (UNWTO) in 2019 revealed that 27% of
the reasons to visit other countries includes also health [5]. That is the second high rate in
terms of reasons, as pointing the importance of traveling for receiving health services. In
terms of economy, business and marketing side, Health Tourism also brought many
advantages for both tourists, service providers, and healthcare staff [6, 7]. Eventually, this
trending area is nowadays in the center of research studies and has a great potential for the
future of healthcare as well as tourism applications.
When the actors of typical Health Tourism strategies are considered, it is clear that
tourists and service providers or medical staff take essential role in terms of effectiveness
and efficiency of applications. Tourists requiring healthcare as well as even medical

International Journal of Information Communication Technology and Digital Convergence
Vol. 8, No. 1, June 2023, pp. 23-33
24
operations do not only think about the costs. They also think about quality of services and
the exact experience they will get during their stay in a different country [2, 8]. On the
other hand, while doctors and medical staff do their best to help tourists to regain health,
the providers (e.g., providers: agencies, hospitals, hotels) need an optimum balance of
income and reputation. So, there are different factors from individual to environmental to
be considered for better Health Tourism. When it is specifically evaluated in terms of
individual differences of tourists (e.g., age, gender, income state, home country, required
health services, touristic interests), the problem expands to a more advanced data analysis
problem. At this point, technology has the role of seeing the big picture and creating
innovations in many service sectors. Since it is very practical for software systems to
gather data from people or planned scenario setups, some advanced methodologies can be
used for useful and even precise decision making. Data Mining has such a way as it hosts
successful techniques, which are connected with also Artificial Intelligence and capable
of creating new knowledge from bigger collection of data [9].
Objective of this paper is to examine the relation between Health Tourism and Data
Mining. In detail, it was aimed to ensure a recent and comprehensive enough look at to
the role of data inside Health Tourism ways, and advancing this role with the effective
touches by Data Mining techniques. Since there is a great variety of data associated with
both tourists and host service providers, there is a need for careful analysis of the target
data and ensuring outcomes, which are describing better about potential tourists, service
outcomes, effectiveness of the healthcare applications, adjusting marketing strategies, and
getting a general picture of the whole ecosystem. Specifically, at the era of Big Data and
even intelligent information technologies, such a careful analysis could be done via
advanced data processing methodologies. So, the Data Mining seems to have a great role
for ensuring innovations inside the Health Tourism. It is thought that outputs from this
paper will enable readers to understand the mentioned role and the future of Health
Tourism through data-sensitive tools.
Considering the general objective of the study, the remaining content of the paper is
organized as follows: The next section is devoted to a general examination about the
digital transformation happening in all fields including the area of Health Tourism. This
section gives a remarkable account of thinking about how opportunities of the digital
world is shaping the way of Health Tourism. After this section, the third section advances
the perspective and discusses about how Data Mining can be effective in terms of present
Health Tourism scenarios. Following to that, the fourth section discusses about the future
opportunities, by taking the current state into account and deriving new ideas. Finally, the
content is ended with the final concluding explanations under the last section.
2. Health Tourism and Digital Transformation
As like many other fields including the human factor, Health Tourism has been adapted
to the needs and changes. Today, it is possible to talk about different types of Health
Tourism. These may include thermal tourism, spa-wellness tourism, elderly tourism,
disabled tourism, and medical tourism [10-12]. As it may be understood the coverage of
different Health Tourism types include tourists with different characteristics for age,
disability, medical needs or more focus on well-being as well as psychological support.
Sometimes, the application ways of Health Tourism may require more than one type
Health Tourism, by targeting specific tourist groups. So, this situation results combination
and management of several Health Tourism scenarios at the same time. At the end, the
time brings also many changes for the literature of Health Tourism. For example, dental
tourism has been a separate sub-type of medical tourism [13, 14].

International Journal of Information Communication Technology and Digital Convergence
Vol. 8, No. 1, June 2023, pp. 23-33
25
In the context of the last decade, information and communication technologies (ICT)
have become essential parts of service-oriented solutions. That caused service-oriented
solutions to become ‘electronic’. As a result, users generally benefit from applications
such as e-trade, e-banking, e-management…etc. That’s actually examined within the topic
of digital transformation [15]. Digital Tourism is a typical umbrella including not only
users (humans) but also computers, communication tools (the related infrastructure,
Internet…etc.), algorithms, scientific essentials, and software platforms. As long as
Health Tourism includes both human factor and needs for remote information-
communication use, the related digital transformation is actually happening inside the
area of Health Tourism. This digital transformation affects all actors enrolled in the
applications widely.
2.1. Existence of Digital Transformation in Health Tourism
In today's conditions, it is possible to indicate that the information from the real world
is critical for all applications enrolled in the digital transformation wind. Considering the
digital form of information, the data is the key element, which is shaping the way of
solutions and the desired outcomes for improving especially service-oriented actions.
Because Health Tourism needs reaching to tourists (patients or customers) in remote
places, use of data is a vital mechanism for today’s conditions. Nowadays, there is an
intense interest in using Web platforms for communication and information sharing (from
social media to financial systems or just for receiving some online service or buying
something). The way of using such platforms have been even carried over mobile
ecosystems. So, development of such systems in the way of Health Tourism purposes is a
requirement with the digital transformation era.
Eventually, existence of digital transformation in Health Tourism is all about use of
data. Data use is about reaching to target audience, planning market strategies, checking
the quality of health services, and managing the economy. Table 1 provides a general
overview for the scenarios of data use in Health Tourism processes.
Table 1. Data use scenarios in Health Tourism.
Data Origin
Scenarios
Tourists
knowing about target audience, running marketing, designing
services, tracking tourists, having feedback
Health Tourism
Agency
tracking operations, managing marketing, tracking
economical resources, managing business
Hotel
tracking services, tracking tourists (customers), managing
facilities, managing touristic opportunities
Medical
Institution
managing equipment and facilities, planning services
Doctors and/or
Healthcare Staff
knowing about doctors and/or healthcare staff, optimizing
services and schedules, tracking performance, having
feedback

International Journal of Information Communication Technology and Digital Convergence
Vol. 8, No. 1, June 2023, pp. 23-33
26
As it may be understood, the data makes sense for digitally transforming the Health
Tourism operations. Although the exact results are seen in the real world, advancing these
results are associated with effective use of data. When the data is considered in terms of
Health Tourism, there are many outcomes enabling people to use information
technologies and dive into the digital world.
2.2. Outcomes by the Digitally Transformed Health Tourism
Digitally transformed Health Tourism brings many advantages when it is thought about
especially effectiveness and efficiency. As data produced by the actors of Health Tourism,
digital tools are capable of processing the data and ensuring results rapidly. Moreover, use
of digital tools is very effective in catching statistical details, which give important
opinions for strategies and policies. In detail, the digital transformation has already caused
use of information and communication technologies (ICT) to improve Health Tourism
quality and experience. This still expanding role of ICT is connected with Web platforms,
e-applications, data processing, and use of advanced algorithmic systems in flow of
Health Tourism applications [16]. By thinking more technology oriented, it is possible to
indicate that the current trend for the Industry 4.0 reveals sensors and software
infrastructure for employing Internet of Things (IoT) ecosystems, mobile tools, robotics
or Artificial Intelligence in terms of Health Tourism aims. In their study, Wong and
Hazley pointed the mentioned multi-ways of using software and hardware support for the
digital transformation in Health Tourism [17]. When accessibility is considered, solutions
by use of Virtual Reality (VR) and Augmented Reality (AR) has opened the doors for
improving the user experience. Although use of these ‘reality’ tools have been widely
examined in the context of general tourism applications, they already found their ways
inside Health Tourism. In the study by Dalkiran, general potentials of using reality tools
were evaluated in detail [18]. As it may be understood, the exact mechanism of reality
applications for cultural and touristic interactions with the locations is combined within
health services or facilities that may be shown again to the user side through the same
interface way. Considering such an advanced software-based expansion, Baran and
Karaca also expressed the active role of multimedia and simulated environments to
improve user experience for Health Tourism [19]. On the other hand, there are recent
research studies examining the role of mobile applications and even wearables in terms of
improving user experience and data collecting capabilities for advancing services. Simsek
et al. examined the wearables within mobile technologies by pointing importance of
mobile / wearable solutions and discussing about opportunities [20]. When combined with
digital tools, user experience in Health Tourism applications is effectively improved. This
improvement may be felt in not only common tourists but also impaired tourists. As
pointed by Kose and Murteza, mobile applications have a remarkable in achieving better
experience for impaired tourists [21]. Of course, the user experience has a wider scope for
improving the roles of all actors (from tourists to healthcare staff, provider / agency
representatives…etc.) inside the Health Tourism. In addition to the mentioned ways so
far, digital transformation has already showed its effect for marketing activities. Called as
digital marketing, the current era of marketing affects the way of promoting Health
Tourism positively. In this context, the literature has been growing greatly thanks to
research studies examining different aspects of digital marketing for Health Tourism [22-
27].
By thinking more specifically, outcomes of the digitally transformed Health Tourism
can be associated with the enrolling actors as follows:

International Journal of Information Communication Technology and Digital Convergence
Vol. 8, No. 1, June 2023, pp. 23-33
27
• In the context of digitally transformed Health Tourism, everything is collected,
processed, used, and eventually stored in the form of data. This allows fast
information analysis and providing instant decision making when needed.
• Thanks to use of ICT, it is easier to reach out any individual (tourists, staff) or
Health Tourism institutions (agencies, hotels, hospitals) around the world.
• By using sensors, IoT tools and even robotic systems, it is easier to collect data
from users and the ecosystem of the Health Tourism. The collected data may be
used for many purposes from tracking to marketing.
• By using ICT and more advanced tools such as VR / AR technologies, user
experience is widely improved for tourists.
• ICT technologies allow better communication and information use around the
world. This is a requirement for comfortable arrangements of Health Tourism
services, traveling around countries, accommodating and even having feedback
for received services.
• Digital tools allow providers / agencies to create innovative digital marketing
applications. As like in many different areas, such digital marketing solutions
cause Health Tourism promoting activities to become advancing in terms of
effectiveness.
• Use of ICT and the associated digital tools are important for improving the quality
of healthcare services in terms of tasks, environments and analysis of feedback by
tourists.
By considering the outcomes discussed so far, it is possible to associate the advantages
of digitally transformed Health Tourism with seven topics: user experience, data analysis,
communication, marketing, information use, quality, and efficiency (Figure 1).
Figure 1. Topics covering advantages of digitally transformed Health Tourism.

International Journal of Information Communication Technology and Digital Convergence
Vol. 8, No. 1, June 2023, pp. 23-33
28
Although the use of data, ICT and the related technological background improves
many aspects of Health Tourism, there is area to expand application scope, improve
outputs and ensure better decision making in different steps of Health Tourism
scenarios. At this point, data analysis and processing tasks need more advanced
technologies such as Big Data, Artificial Intelligence, and Data Mining. Actually,
the concept of Big Data is used for defining the current form of data, which is
combined fast by many sources, with great complexity, value and update rates [28].
On the other hand, Artificial Intelligence is the form of advanced algorithms, which
are able to ensure flexible solutions for problem cases and even learn from the data
for effective future solutions [29]. Finally, the concept of Data Mining is used for
algorithms, which are able to create new knowledge from the data [30]. Actually,
many algorithms are shared under umbrellas of both Artificial Intelligence and Data
Mining. However, the research studies benefit from both area with some specific
approach differences. As a result of increasing amount of data to use in digital
solutions of Health Tourism, Data Mining has an increasing importance for effective
and efficient processing of data and deriving useful outputs. By the year of 2023,
Data Mining is still a niche solution way inside Health Tourism. So, it is remarkable
to take a general look at to the present state of the literature and discuss about how
Data Mining can be applied for Health Tourism problems.
3. Data Mining for Health Tourism
With the role of ‘creating new knowledge’, Data Mining took interests of many
research fields. Data Mining stands over the background of Statistics as well as
Mathematics and employs algorithmic solutions for deriving descriptive or predictive
knowledge [31]. Briefly, descriptive knowledge is about explaining the current
information better whereas predictive knowledge is for especially predictions for future
states [31, 32]. When it is considered for the solutions in Health Tourism, it is possible to
see some research studies done so far. In their study, Koh and Gan performed a Data
Mining approach (especially based on clustering) to understand factors regarding tourists’
choice of hospital type (stay) in Singapore. In this way, it aimed to understand the gap
between the first expectations and the resulting experience after the hospital stay [33].
The study done by Molaee Fard considered the use of Data Mining for developing a
recommendation system for tourists looking for places to receive healthcare. In the study,
use of Data Mining techniques achieved more than %90 success rates generally [34]. As
another study on recommendation systems, Panteli et al. used Data Mining to match
tourists with healthcare and tourism service providers / agencies. In detail, the Data
Mining approach used the characteristic data of both sides to achieve the matching
solution [35]. In a very recent study, Rashid et al. focused on the way of IoT and Data
Mining for the Health Tourism purposes, by directing their research to the Malaysia scope
[36]. By accepting sports tourism outcomes within the Health Tourism area, it is possible
to indicate that the study by Yu et al. used Data Mining for examining spatial
characteristics of sport tourism destinations [37]. By accepting the rural tourism outcomes
serving to also Health Tourism somehow, Chen et al. employed Data Mining for rural
tourism path development, by moving from the Big Data [38]. In a very recent study,
Ridderstaat run time-series Data Mining to analyze outbound Health Tourism demand
situation and also price developments in the case of USA [39].
Since they are not directly associated with Health Tourism, research studies including
Data Mining and general tourism problems were not included in this study. So, it may be
seen that there is a great opportunity to employ Data Mining inside exact problems of
Health Tourism literature. In order to achieve that, it is important to explain some about
methodologies available for Health Tourism research.

International Journal of Information Communication Technology and Digital Convergence
Vol. 8, No. 1, June 2023, pp. 23-33
29
3.1. Methodologies for Applying Data Mining in Health Tourism
Since problem formulation finds always same way in different fields, widely known
methodologies of Data Mining can be effectively used for Health Tourism problems. At
this point, it is important to formulate the target problems in the view of inputs and
outputs. This means if it is possible to e.g., have some idea about tourists’ destination
choices by taking a look at their specific interests, then it is possible to have interest data
as input and destination choices as output. A common working mechanism of Data
Mining algorithms is that it is needed to train / adapt them to the known input-output data
samples. Of course, it may be required to have some data processing steps to make data
simpler for Data Mining algorithms, by including e.g., factor analysis, data cleaning,
missing data management or additional statistical actions.
Methodologies of Data Mining can be classified under (1) Regression, (2)
Classification, (3) Clustering, and (4) Association Rules (Figure 2) [31, 32]. Use of
regression is for predictive numerical outcomes, which may include demand, economical
rates, interest rates…etc. On the other hand, classification may be applied for again
predictive outcomes in the context of labeled outputs (classes) e.g., destination choices,
classifying quality, service, structuring plans. In the context of descriptive analysis,
Clustering may be used for grouping similar tourists, providers (agencies, hospitals),
healthcare plans, and destinations for better matching / recommendations. As another
descriptive solution, Association Rules may be applied to determine which factors may be
considered together for e.g., healthcare plans for a tourist, actions to take healthcare-
tourism related schedule. Table 2 provides wider examples for Health Tourism
application ways within each Data Mining methodology.
Figure 2. Methodologies of Data Mining [31, 32].
Table 2. Health Tourism application ways with Data Mining.
Data Mining
Methodology
Examples for Health Tourism Applications
Regression
• Predicting demand by tourists
• Prediction of economical income-outcome
• Prediction of costs for specific plans
• Finding out possible success rates of plans
• Predicting potential tourists’ numerical features

International Journal of Information Communication Technology and Digital Convergence
Vol. 8, No. 1, June 2023, pp. 23-33
30
• Having idea about numerical work load
• Predicting quality rates of services / operations
Classification
• Classifying potential destinations for tourists
• Classifying tourists for marketing purposes
• Finding out recommendations among actors
• Planning schedules, routes for touristic activities
• Determining priorities of tourists’ needs
• Defining plans for specific tourists
• Predicting tourists’ satisfaction levels
• Classifying services / operations for future matching
• Classifying user experience, quality, strategy…etc.
Clustering
• Determining tourists with common interests, needs
• Grouping plans, services for recommendations
• Defining different plans for specific tourists
• Managing resources, healthcare / medical staff
• Grouping destinations according to specific features
Association
Rules
• Understanding common parts of successful plans
• Determining common features of tourist groups
• Detecting inside details of plans for specific tourists
• Matching tourists with destinations, service providers
4. Future Perspectives
According to the present state as well as methodological scope of Data Mining, it may
be critical to have some future perspectives. It is important that especially technological
advancements and connection of Health Tourism with rising ICT and tools are very
important for the future state. So, based on this fact, some ideas about the future can be
explained generally as follows:
• Because IoT systems, wearables and mobile technologies became already
common objects of the daily life, future will be more open for gathering from
users. So, this will include detailed, analysis of data, resulting to smart platforms
of tourist-provider interaction.
• In the future, use of Big Data will continue to improve role of Data Mining in
Health Tourism. That means even users’ social media actions or daily life
activities will be feeding Health Tourism data processing algorithms for
descriptive and predictive analysis.
• The future will include more modular, collaboration Data Mining systems, which
are capable of applying regression, classification, clustering or association rules to
derive separate output data, which are then combined for common outputs.
• The future of Health Tourism will be often associated with user experience,
marketing and business-based aims. So, Data Mining will be used more in such
problem orientations to improve Health Tourism for both tourists and providers.
• In the future, there may be more global systems of Data Mining algorithms, which
include common policies and rules determined by Health Tourism councils. These
will be used by service providers so that a global flow of Health Tourism will be
improved accordingly.

International Journal of Information Communication Technology and Digital Convergence
Vol. 8, No. 1, June 2023, pp. 23-33
31
• Considering Health Tourism actors of the future, people having expertise in
computational and scientific solutions including Data Mining will enroll more in
Health Tourism applications. This may open opportunities in new research areas
inside Health Tourism and Data Mining relation.
• Health Tourism seems connected with recommendations and decision making.
So, the future problem formulations of Health Tourism (for Data Mining), will
often be associated with that.
5. Conclusion
This study provided a general view regarding the role of Data Mining in Health
Tourism applications. As it is known, digital tools and the active role of ICT haven been
changing the way service-oriented tasks, by considering especially active role of data.
Such change has been examined with the term of digital transformation and it is clear that
the Health Tourism has been widely improved thanks to that transformation happening. In
detail, more advanced data analysis and processing methodologies have found their way
already in technologies such as Artificial Intelligence and Data Mining. By including
methodologies of regression, classification, clustering and association analysis, especially
Data Mining is one practical, scientific way to derive meaningful new knowledge from
already known data. As discussed in the study, active role of Data Mining algorithms can
support better analysis of complex data to lead more effective decision making. In this
context use of Data Mining has a great potential to ensure predictions or make better
descriptions for the used Health Tourism data. Such analysis may be done with not only
stored data but also real-time flowing data. So, the future of Health Tourism is highly
associated with Data Mining use. Currently, there is an interest to apply Data Mining in
specific applications for improving user experience as well as planning capabilities of
agencies. However, there is still many opportunities to use Data Mining accordingly for
establishing more advanced ways of Health Tourism. The authors suggest the interested
researchers to think about desired outcomes awaited in specific Health Tourism
applications. After this first step, the next task is to design the structure of the problem
model according to appropriate Data Mining methodologies and available data. At the
end, it is believed that successful applications of the modeled Data Mining will lead to
innovative developments in the area of Health Tourism.
Acknowledgement
This article is associated with the MSc. thesis study done by G. Kose under the
supervision by Dr. O. E. Colakoglu.
6. References
[1] Reisman, D. A. Health tourism: Social welfare through international trade. Edward Elgar Publishing,
(2010).
[2] Korkmaz, M., Aytac, A., Yucel, A. S., Kilic, B., Toker, F., Gumus, S. Health tourism in Turkey and
practical example of its economic dimensions. IIB International Refereed Academic Social Sciences
Journal, 5(15), (2014), 229.
[3] Smith, M. K., Puczko, L. (Eds.). The Routledge handbook of health tourism. Taylor & Francis, (2016).
[4] Swain, D., Sahu, S. Opportunities and challenges of health tourism in India. In Conference on Tourism
in India–Challenges Ahead (Vol. 15, No. 17, pp. 245-254), (2008).
[5] UNWTO. 2019, International Tourism Highlights. Retrieved from https://www.e-
unwto.org/doi/pdf/10.18111/9789284421152 10.06.2023.

International Journal of Information Communication Technology and Digital Convergence
Vol. 8, No. 1, June 2023, pp. 23-33
32
[6] Eusebio, C., Carneiro, M. J., Kastenholz, E., Alvelos, H. The economic impact of health tourism
programmes. Quantitative methods in tourism economics, (2013), 153-173.
[7] Puczko, L. Health, Wellness and Tourism: healthy tourists, healthy business. In Proceeding of the
Travel and Tourism Research Association Europe 2010 Annual Conference (pp. 1-3), (2010).
[8] Jyothis, T., Janardhanan, V. K. Service quality in health tourism: An evaluation of the health tourism
providers of Kerala (India). South Asian Journal of Tourism and Heritage, 2(1), (2009), 77-82.
[9] Wu, X. Data mining: artificial intelligence in data analysis. In Proceedings. IEEE/WIC/ACM
International Conference on Intelligent Agent Technology (IAT 2004) (pp. 7-7). (2004), IEEE
Computer Society.
[10] Ozsari, S. H., Karatana, O. Health Tourism Situation in Turkey (In Turkish). Journal of Kartal Training
& Research Hospital, 24(2), (2013), 136-144.
[11] Isa, S. M., Ismail, H. N., Fuza, Z. I. M. Elderly and heritage tourism: A review. In IOP Conference
Series: Earth and Environmental Science (Vol. 447, No. 1, p. 012038). (2020), IOP Publishing.
[12] Ormond, M. Medical tourism. The Wiley Blackwell Companion to Tourism, (2014), 425-434.
[13] Jaapar, M., Musa, G., Moghavvemi, S., Saub, R. Dental tourism: Examining tourist profiles, motivation
and satisfaction. Tourism Management, 61, (2017), 538-552.
[14] Akbar, F. H., Pasiga, B. D., Samad, R., Rivai, F., Abdullah, A. Z., Awang, A. H., Pratiwi, D. The
relationship between service quality, culture similarity to satisfaction and loyalty of medical (dental)
tourism. Systematic reviews in pharmacy, 11(12), (2020), 19-30.
[15] Zaoui, F., Souissi, N. Roadmap for digital transformation: A literature review. Procedia Computer
Science, 175, (2020), 621-628.
[16] Oduncuoglu, F., Kose, G., Dogan, O., Deniz, E. Digital Transformation Current Trends in Health
Tourism: A Theoretical Study (In Turkish). ATLAS 10. International Social Sciences Congress, (2023),
116-126.
[17] Wong, B. K. M., Hazley, S. A. S. A. The future of health tourism in the industrial revolution 4.0
era. Journal of Tourism Futures, 7(2), (2020), 267-272.
[18] Dalkiran, G. B. Health Tourism Components and Intermediary Institutions as Supplier Businesses in the
Context of Health 4.0. In Health 4.0 and Medical Supply Chain (pp. 147-162). (2023), Singapore:
Springer Nature Singapore.
[19] Baran, Z., Karaca, S. Next-Generation Technologies in Health Tourism. In Global Perspectives on the
Opportunities and Future Directions of Health Tourism (pp. 138-164). (2023), IGI Global.
[20] Simsek, F., Bektas, G., Gemlik, H. N. Availability and Importance of Wearable Technologies for
Mobile Health Applications in the Scope of Health Tourism. In 3rd International Conference on
Tourism: Theory, Current Issues and Research, (2017), 67-75.
[21] Kose, G., Murteza, F. Disabled Friendly Mobile Services in Health Tourism and Effective
Communication (In Turkish). ATLAS 10. International Social Sciences Congress, (2023), 819-833.
[22] Tengilimoglu, D., Guzel, A., Aykan, C., Tengilimoglu, E., Boduroglu, E. The evaluation of the digital
marketing activities of thermal facilities and JCI accredited hospitals in terms of health
tourism. International Journal of Health Management and Tourism, 3(2), (2018), 108-121.
[23] Bektas, G., Demirel, S., Olmez, F. The importance of digital marketing in health tourism. In 3rd
International Conference on Tourism: Theory, Current Issues and Research, (2017), 76-83.
[24] Deniz, E., Dogan, O., Kose, G. Potential of Reality Applications for Experiential Marketing in Health
Tourism (In Turkish). Ahi Evran 3. International Conference on Scientific Research, (2023), 288-298.
[25] Halkiopoulos, C., Dimou, E., Kompothrekas, A., Telonis, G., Boutsinas, B. The E-tour facilitator
platform supporting an innovative health tourism marketing strategy. In Culture and Tourism in a
Smart, Globalized, and Sustainable World: 7th International Conference of IACuDiT, Hydra, Greece,
2020 (pp. 609-623). (2021), Cham: Springer International Publishing.
[26] Ostojic, B., Bogdanovic, M., Ruzic, J. The Impact of Digital Marketing on Health Tourism in the
Western Balkans during the COVID-19 Pandemic. 6th International Thematic Monograph: Modern
Management Tools and Economy of Tourism Sector in Present Era, (2022), 393-406.

International Journal of Information Communication Technology and Digital Convergence
Vol. 8, No. 1, June 2023, pp. 23-33
33
[27] Bayev, V. V., Bayeva, O. V., Bakhov, I. S., Antonenko, I. Y., Melnyk, I. L. Digital marketing as an
innovative communication tool in the health tourism system. SSRG International Journal of Engineering
Trends and Technology, 68(8), (2020), 36-40.
[28] Sagiroglu, S., Sinanc, D. Big data: A review. In 2013 international conference on collaboration
technologies and systems (CTS) (pp. 42-47). (2013), IEEE.
[29] Hunt, E. B. Artificial Intelligence. (2014), Academic Press.
[30] Gullo, F. From patterns in data to knowledge discovery: What data mining can do. Physics Procedia, 62,
(2015), 18-22.
[31] Jothi, N., Husain, W. Data mining in healthcare–a review. Procedia Computer Science, 72, (2015), 306-
313.
[32] Siraj, F., Abdoulha, M. A. Mining enrolment data using predictive and descriptive
approaches. Knowledge-Oriented Applications in Data Mining, (2007), 53-72.
[33] Koh, H. C., Gan, L. L. A data mining analysis of healthcare tourism in Singapore. Advances on
Information Processing and Management, (2009), 26.
[34] Molaee Fard, R. Developing a recommender system for the health tourism industry using data mining
methods. Engineering Management and Soft Computing, 7(2), (2022), 195-220.
[35] Panteli, A., Kompothrekas, A., Halkiopoulos, C., Boutsinas, B. An innovative recommender system for
health tourism. In Culture and Tourism in a Smart, Globalized, and Sustainable World: 7th International
Conference of IACuDiT, Hydra, Greece, 2020 (pp. 649-658). (2021), Cham: Springer International
Publishing.
[36] Rashid, I. M. A., Rawabdeh, M., Qudah, A. A., Samah, I. H. A., Amlus, M. H., Wan Husain, W. A. F.
Analyzing the influence of internet of things (IoT) data mining in medical tourism industry. In AIP
Conference Proceedings (Vol. 2608, No. 1). (2023), AIP Publishing.
[37] Yu, S., Qiu, C., Yang, R. Spatial characteristics of sports tourism destination system based on data
fusion and data mining. Mobile Information Systems, 2022, (2022), 1897852.
[38] Chen, L., Hu, H., He, X., Lyu, M. The development path and data mining mode of rural tourism under
the background of big data. Wireless Communications and Mobile Computing, 2022, (2022), 1-13.
[39] Ridderstaat, J. Measuring hidden demand and price behavior from US outbound health tourism
spending. Tourism Economics, 29(3), (2023), 759-787.