Ensemble Models for Classification of Coronary Artery Disease using Decision Trees

Abstract

The foundation of data mining techniques using decision tree methods played a crucial role in the identification and classification of diseases. In the utilization of decision tree classifiers to develop the robust classifier for classification of Coronary Artery Disease data set namely Z-Alizadeh Sani and extension Z-Alizadeh Sani. We have used three decision tree techniques Random Forest (RF), Classification and Regression Tree (CART), J48 (C4.5) and made two ensemble models. These ensemble models have different combining rules like voting and stacking. The Voting Scheme model Vote (J48, RF, CART) and stacking Scheme model Stack (J48, RF, CART) have our proposed model. The findings are compared in individual and ensemble models classifier with 5-Fold Cross-Validation and 10-Fold Cross-Validation. The finding of the proposed ensemble models can be used in the detection and evaluation of Coronary Artery Disease (CAD).