WVEHDD: Weighted Voting based Ensemble System for Heart Disease Detection

Abstract

Although several machine learning (ML) based
algorithms are proposed by various researchers for heart disease
detection (HDD), most of these works considered a very small
experimental dataset to justify the efficiency of ML techniques in
HDD. Moreover, despite of the low correlation of the features
with the target, all the features were used for HDD. Considering
the limitations of these existing systems, current study
emphasizes on the designing of a Weighted Voting based
Ensemble (WVE) Classifier for HDD from a sufficiently large
dataset comprising of 1296 instances. Although there are 13
features, only 4 features are found to be statistically significant in
HDD. For designing an efficient WVE classifier for HDD, the
weighted votes of five efficient classifiers are combined to get the
final decision. The experimental result shows that the proposed
WVEHDD system outperforms the existing systems by providing
the highest train accuracy of 96.15% and test accuracy of
95.64%.