Introduction: Since medical tourism is considered as an incremental activity in this sector and proper infrastructures in country to make medical tourism are lacking, announcement by authorities to provide perquisites of medical tourism to make the first clinic hotel and health town are necessary for the purpose of developing medical tourism in Iran, all of these side issues should be investigated. Methods: Cardiovascular diseases are very common because of pollution and industrial development. In this research, by extracting related studies on medical tourism and its localization, using hospitals data bank and questionnaire of 640 medical tourists, only 528 of them were approved and finally by using IBM SPSS modular 14.1 software decision tree in data algorithm, efficiency and purity level were obtained. The method for data preprocess step is utilized to extract the best model. Two preprocess steps are deleting useless and correlated features, because data should be prepared until model has the least error. Results: Among the examined algorithm including C&RT, CHAID and Multiple Linear Regression, it was shown that C&RT has the optimal results. The results obtained from this research indicate that C&RT binary decision tree has the smallest error value (0.078) and the greatest accuracy value (0.922). Conclusion: In this research, first effective factors on heart medical tourism were investigated with the help of experts in this field, then C&RT and CHASID models using Clementine software and multiple linear regression variables were compared and ranked. With respect to this algorithm, personnel behavior, social security and communication variables are respectively the most important factors for medical tourist attraction.
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