MV5: A Clinical Decision Support Framework for Heart Disease Prediction Using Majority Vote Based Classifier Ensemble

The medical diagnosis process can be interpreted as a decision-making process during which the physician induces the diagnosis of a new and unknown case from an available set of clinical data and using his/her clinical experience. This process can be computerized in order to present medical diagnostic procedures in a rational, objective, accurate and efficient way. In the last few decades, many researchers have focused on developing effective methods for intelligent heart disease prediction and decision support systems. For such a system, high accuracy of prediction is paramount. In this research, an ensemble classifier is proposed, which uses majority vote-based scheme for heart disease data classification and prediction. The five heterogeneous classifiers used to construct the ensemble model are as follows: Naïve Bayes, decision tree based on Gini Index, decision tree based on information gain, memory-based learner and support vector machine. Five datasets from different data repositories are employed for testing the effectiveness of the ensemble model. Each dataset has different types of attributes, for instance binary, real, continuous, categorical, etc. Experimental results with stratified cross validation show that the proposed MV5 framework deals with all the attribute types. MV5 has achieved an accuracy of 88.52% with 86.96% sensitivity, 90.83% specificity and 88.85% f-measure. Comparison of proposed MV5 model with individual classifiers shows increase in average accuracy, sensitivity, specificity and f-measure of about 14, 11, 17 and 18% respectively.