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

Kanak Kalita; Narayanan Ganesh; Sambandam Jayalakshmi; Jasgurpreet Singh Chohan; Saurav Mallik; Hong Qin

doi:10.3389/fdgth.2023.1279644

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

Front Digit Health. 2023 Nov 16:5:1279644. doi: 10.3389/fdgth.2023.1279644. eCollection 2023.

Authors

Kanak Kalita¹, Narayanan Ganesh², Sambandam Jayalakshmi³, Jasgurpreet Singh Chohan⁴, Saurav Mallik⁵, Hong Qin⁶

Affiliations

¹ Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai, India.
² School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.
³ Department of Master of Computer Applications, MEASI Institute of Information Technology, Chennai, India.
⁴ Department of Mechanical Engineering and University Centre for Research & Development, Chandigarh University, Mohali, India.
⁵ Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, United States.
⁶ Department of Computer Science and Engineering, University of Tennessee at Chattanooga, Chattanooga, TN, United States.

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.

Keywords: XGBoost; classification; deep belief network; feature selection; heart disease; optimization.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article.