M. Shamsollahi - Industrial Engineering Department, University of Science & Technology, Tehran, Iran.
A. Badiee - Industrial Engineering Department, University of Science & Technology, Tehran, Iran.
M. Ghazanfari - Industrial Engineering Department, University of Science & Technology, Tehran, Iran.

Heart disease is one of the major causes of morbidity in the world. Currently, large proportions of healthcare data are not processed properly, thus, failing to be effectively used for decision making purposes. The risk of heart disease may be predicted via investigation of heart disease risk factors coupled with data mining knowledge. This paper presents a model developed using combined descriptive and predictive techniques of data mining that aims to aid specialists in the healthcare system to effectively predict patients with Coronary Artery Disease (CAD). To achieve this objective, some clustering and classification techniques are used. First, the number of clusters are determined using clustering indexes. Next, some types of decision tree methods and Artificial Neural Network (ANN) are applied to each cluster in order to predict CAD patients. Finally, results obtained show that the C&RT decision tree method performs best on all data used in this study with 0.074 error. All data used in this study are real and are collected from a heart clinic database.

data mining, coronary heart disease, Clustering, Classification, decision tree