Classification of Heart Disease Using Cluster Based DT Learning

Abstract

In the rural side, due to the absence of cardiovascular ailment centers, around 12 million people passing away worldwide reported by WHO. The principal purpose of coronary illness is a propensity of smoking. Our Cluster based disease Diagnosis (CDD) applies the ML classifiers to improve the prediction accuracy of cardiovascular diseases. For this we have taken a real Cleveland dataset from UCI. First, the ML performance is evaluated through all features. Then, the dataset is split through the class pairs through its distribution. From this class pair, the significant features are identified through entropy process. Through our CDD approach four significant features are identified from thirteen features. From this four features, the ML performance increases when compared to all other features. That is, in RF model the accuracy improves to 9.5%, SVM by 7.2% and DT model by 2.3%.