Abstract
Abstract: Heart disease is the leading problem of death globally, and early detection is pivotal in preventing the progression of the disease. In this paper, Improved EnsembleSVM method is proposed for the prediction of heart disease risk. The technique involves randomly partitioning the dataset into smaller subsets using a mean based splitting approach. Oversampling of imbalanced data introduces the extension of Synthetic Minority Over-sampling Technique through a recent ideology, and recurrent ensemble-based noise filter called duplicative-Partitioning Filter, which can overwhelm the hindrance fashioned by noisy and frontier models in overbalanced dataset. Ensemble classifier with oversampling technique plays an accurate result for predict heart disease in efficient way.
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