A hybrid cost-sensitive ensemble for heart disease prediction

Qi Zhenya,Zuoru Zhang

doi:10.1186/s12911-021-01436-7

Abstract

BackgroundHeart 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.MethodsA cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method 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 was better than individual classifiers and the contribution of Relief algorithm.ResultsThe best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm.ConclusionsThe proposed ensemble 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.

Highlights

Heart disease is the primary cause of morbidity and mortality in the world
For Hungarian dataset, Slope, Ca and Thal are deleted during the process of missing-value imputation because these features have missing values more than 50% of all instances
In this study, a cost-sensitive ensemble method based on five different classifiers is presented to assist the diagnosis of heart disease

Summary

Introduction

Heart disease is the primary cause of morbidity and mortality in the world. The diagnosis of heart disease is difficult because there are too many factors to analyze. As the leading cause of death, heart disease is responsible for nearly 30% of the global deaths annually [2]. As it’s associated with numerous symptoms and various pathologic features such as diabetes, smoking and high blood pressure, the diagnosis of heart disease remains a huge problem for less experienced physicians [6]. ECG may fail to detect the symptoms of heart disease in its record [7] while CA is invasive, costly and needs highly-trained operators [8]

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Conclusion