A Hybrid Cost-sensitive Ensemble for Heart Disease Prediction

Overview

Journal BMC Med Inform Decis Mak

Publisher Biomed Central

Specialty Medical Informatics

Date 2021 Feb 26

PMID 33632225

Citations 7

Authors

Qi Zhenya

Zuoru Zhang

Affiliations

Soon will be listed here.

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 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.

Results: The 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.

Conclusions: The 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.

Citing Articles

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A cost-sensitive deep neural network-based prediction model for the mortality in acute myocardial infarction patients with hypertension on imbalanced data.

Zheng H, Sherazi S, Lee J Front Cardiovasc Med. 2024; 11:1276608.

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Enhancing heart disease prediction using a self-attention-based transformer model.

Rahman A, Alsenani Y, Zafar A, Ullah K, Rabie K, Shongwe T Sci Rep. 2024; 14(1):514.

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A comparative analysis of meta-heuristic optimization algorithms for feature selection on ML-based classification of heart-related diseases.

Ay S, Ekinci E, Garip Z J Supercomput. 2023; 79(11):11797-11826.

PMID: 37304052 PMC: 9983547. DOI: 10.1007/s11227-023-05132-3.

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review.

Suri J, Bhagawati M, Paul S, Protogerou A, Sfikakis P, Kitas G Diagnostics (Basel). 2022; 12(3).

PMID: 35328275 PMC: 8947682. DOI: 10.3390/diagnostics12030722.

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