Integrating Decision Tree and K-Means Clustering with Different Initial Centroid Selection Methods in the Diagnosis of Heart Disease Patients

Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease patients. Decision Tree is one of the data mining techniques used in the diagnosis of heart disease showing considerable success. K-means clustering is one of the most popular clustering techniques; however initial centroid selection strongly affects its results. This paper investigates integrating k-means clustering with decision tree in the diagnosis of heart disease patients. It also investigates different methods of initial centroid selection of the k-means clustering such as inlier, outlier, range, random attribute values, and random row methods in the diagnosis of heart disease patients. The results show that integrating k-means clustering with decision tree with different initial centroid selection could enhance the decision tree accuracy in the diagnosing heart disease patients. It also showed that the inlier initial centroid selection method could achieve higher accuracy than other initial centroid selection methods in the diagnosis of heart disease patients. Keywords-Data Mining, K-Means Clustering, Initial Centroid Selection Methods, Decision Tree, Heart Disease Diagnosis.