A Model to Predict Heart Disease Treatment Using Data Mining - Sajad Mazaheri; Maryam Ashoori; Zeynab Bechari (Tehran University of Medical Sciences, 2017)
Background and Aim: Nowadays heart disease is very common and is a major cause of mortality. Proper and early diagnosis of this disease is very important. Diagnostic methods and treatments of the disease are so expensive and have many side effects. Therefore, researchers are looking for cheaper ways to diagnose it with high precision. This study aimed to identify a model for the treatment of heart disease. Materials and Methods: In this descriptive cross-sectional study, the sampling method was census. The sample consisted of data from Khatam and Ali Ibn Abi Talib Hospitals in Zahedan. The data were developed as an Excel file, and Clementine12.0 software was used for data analysis. In the present study, C5.0, C & R Tree, CHAID, and QUEST algorithms and artificial neural network were carried out on the collected data.  Results: The accuracy of 76.04 by C & R algorithm indicates the better performance of Decision Tree Algorithms than that of the Neural Network.  Conclusion: This study aimed to provide a model for the prediction of a suitable heart disease treatment to reduce treatment costs and provide better quality of services for physicians. Due to considerable implementation risks of invasive diagnostic procedures such as angiography and also obtaining successful experiences of data analysis in medicine, this study has presented a model based on data analysis techniques. The improvable point of this model is the provision of a decision support system to help physicians to increase the accuracy of diagnosis in the treatment of diseases.