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 ques-tionnaire. 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 Er-ror, 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.