Investigation of Factors Affecting Choice of Medical Travel Destination Using Data Mining Techniques.
Introduction: Medical tourism, one of the most profitable industries, has been growing rapidly in recent years. Especially Turkey, which has a high ranking among medical travel destinations, has some advantages that can become preferable for international patients. This study is among the first few...
| Publicado en: | International Journal of Travel Medicine & Global Health Vol. 11; no. 1; pp. 186 - 194 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
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Tarbiat Modares University Press
Mar2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164002803&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164002803 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23221100 FUA0 jtl: International Journal of Travel Medicine & Global Health issn: 23221100 maglogo: N pubinfo: dt: Mar2023 vid: 11 iid: 1 pid: 93586 pub: Tarbiat Modares University Press artinfo: ui: 164002803 164002803 164002803 10.30491/IJTMGH.2022.364468.1316 164002803 ppf: 186 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Investigation of Factors Affecting Choice of Medical Travel Destination Using Data Mining Techniques. aug: au: Jenizeh, Sevda Janalipour Ersöz, Filiz affil: Department of Industrial Engineering, Karabuk University, Turkey sug: subj: Data Mining Utilization Medical Tourism Decision Making Patient Preference Human Turkiye Female Male Questionnaires Algorithms Deep Learning Decision Trees Random Forest Support Vector Machine Correlation Coefficient Technology, Medical Descriptive Statistics Female Male ab: Introduction: Medical tourism, one of the most profitable industries, has been growing rapidly in recent years. Especially Turkey, which has a high ranking among medical travel destinations, has some advantages that can become preferable for international patients. This study is among the first few studies which examine affecting factors in patients' medical travel destination choices with Data Mining techniques. Methods: The data were obtained from patients who came to Ankara from abroad for treatment in May 2015 through a question-naire. Cross-industry Standard Process for data mining, known as the CRISP-DM method, is used in this study. After cleaning out the missing data, the models were created using classification algorithms. Results: Models including Generalized Linear Model, Deep Learning, Decision Tree, Random Forest, Gradient Boosted Trees, and Support Vector Machine (SVM) were compared, and SVM reached the best performance with 0.2% Relative Error, 0.014 Root Mean Squared Error and 0.998 Correlation. As a result of the SVM model, effective attributes in patients' satisfaction level include low price advantage, advertisement, doctors with high-quality education, trained assistant staff, relatives living in Turkey, and high technology of medical equipment, respectively. Conclusion: Special attention should be paid to these factors in developing plans and policies for the health tourism sector. However, the importance of related socio-demographic variables was indicated in detail. Eventually, some suggestions were presented to improve the weaknesses in the health tourism sector. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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