Ensemble Based Hybrid Variable Selection Method for Heart Disease Classification
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Abstract
In this paper, we proposed an ensemble-based hybrid variable selection model that aggregates various variable selection methods results based on majority voting approach to select a risk features subset in the heart disease datasets. The performance of the devised framework is evaluated using Z-Alizadeh Sani heart disease dataset from the UCI repository. Besides, we also compare this devised method with three non-ensemble variable selection methods namely the Chi-square test, Recursive Feature Elimination, and L1-Regularization. The selection process of the devised method is validated through a random forest classifier, it performs better in terms of specificity, sensitivity, accuracy, precision, and f1-score. The proposed method significantly enhances the accuracy of the heart disease classification model.
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