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Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction

Authors :
Kanak Kalita
Narayanan Ganesh
Sambandam Jayalakshmi
Jasgurpreet Singh Chohan
Saurav Mallik
Hong Qin
Source :
Frontiers in Digital Health, Vol 5 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

The global rise in heart disease necessitates precise prediction tools to assess individual risk levels. This paper introduces a novel Multi-Objective Artificial Bee Colony Optimized Hybrid Deep Belief Network and XGBoost (HDBN-XG) algorithm, enhancing coronary heart disease prediction accuracy. Key physiological data, including Electrocardiogram (ECG) readings and blood volume measurements, are analyzed. The HDBN-XG algorithm assesses data quality, normalizes using z-score values, extracts features via the Computational Rough Set method, and constructs feature subsets using the Multi-Objective Artificial Bee Colony approach. Our findings indicate that the HDBN-XG algorithm achieves an accuracy of 99%, precision of 95%, specificity of 98%, sensitivity of 97%, and F1-measure of 96%, outperforming existing classifiers. This paper contributes to predictive analytics by offering a data-driven approach to healthcare, providing insights to mitigate the global impact of coronary heart disease.

Details

Language :
English
ISSN :
2673253X
Volume :
5
Database :
Directory of Open Access Journals
Journal :
Frontiers in Digital Health
Publication Type :
Academic Journal
Accession number :
edsdoj.3da5bfac91ce410d8c75e590fbec8b6a
Document Type :
article
Full Text :
https://doi.org/10.3389/fdgth.2023.1279644