WVEHDD: Weighted Voting based Ensemble System for Heart Disease Detection
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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%
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