Using Decision Trees in Data Mining for Predicting Factors Influencing of Heart Disease

Statistics from the World Health Organization (WHO) shows that heart disease is one of the leading causes of mortality all over the world. Because of the importance of heart disease, in recent years, many studies have been conducted on this disease using data mining. The main objective of this study is to find a better decision tree algorithm and then use the algorithm for extracting rules in predicting heart disease. Cleveland data, including 303 records are used for this study. These data include 13 features and we have categorized them into five classes. In this paper, C5.0 algorithm with a accuracy value of 85.33% has a better performance compared to the rest of the algorithms used in this study. Considering the rules created by this algorithm, the attributes of Trestbps, Restecg, Thalach, Slope, Oldpeak, and CP were extracted as the most influential causes in predicting heart disease