Decision Tree Algorithms for Prediction of Heart Disease

Abstract

In the present scenario, maximum causes of death are heart disease. Many researches are taking place to detect all types of heart diseases at very early stage. Scientists are using various computational techniques to predict and prevent heart diseases. Using data mining techniques, the number of tests that are required for the detection of heart disease reduces. In this paper, hybridization technique is proposed in which decision tree and artificial neural network classifiers are hybridized for better performance of prediction of heart disease. This is done using WEKA. To validate the performance of the proposed algorithm, tenfold validation test is performed on the dataset of heart disease patients which is taken from UCI repository. The accuracy, sensitivity, and specificity of the individual classifier and hybrid technique are analyzed.