A hybrid cost-sensitive ensemble for heart disease prediction

Abstract

Background: Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What’s more, the misclassification cost could be very high. Methods: A cost-sensitive ensemble model was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed model contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble model was better than individual classifiers and the contribution of Relief algorithm. Results: The best performance was achieved by the proposed model according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed model was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions: The proposed ensemble model gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.