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Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10687430/ https://www.ncbi.nlm.nih.gov/pubmed/38034907 http://dx.doi.org/10.3389/fdgth.2023.1279644 |
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author | Kalita, Kanak Ganesh, Narayanan Jayalakshmi, Sambandam Chohan, Jasgurpreet Singh Mallik, Saurav Qin, Hong |
author_facet | Kalita, Kanak Ganesh, Narayanan Jayalakshmi, Sambandam Chohan, Jasgurpreet Singh Mallik, Saurav Qin, Hong |
author_sort | Kalita, Kanak |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10687430 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106874302023-11-30 Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction Kalita, Kanak Ganesh, Narayanan Jayalakshmi, Sambandam Chohan, Jasgurpreet Singh Mallik, Saurav Qin, Hong Front Digit Health Digital Health 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. Frontiers Media S.A. 2023-11-16 /pmc/articles/PMC10687430/ /pubmed/38034907 http://dx.doi.org/10.3389/fdgth.2023.1279644 Text en © 2023 Kalita, Ganesh, Jayalakshmi, Chohan, Mallik and Qin. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Digital Health Kalita, Kanak Ganesh, Narayanan Jayalakshmi, Sambandam Chohan, Jasgurpreet Singh Mallik, Saurav Qin, Hong Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction |
title | Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction |
title_full | Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction |
title_fullStr | Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction |
title_full_unstemmed | Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction |
title_short | Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction |
title_sort | multi-objective artificial bee colony optimized hybrid deep belief network and xgboost algorithm for heart disease prediction |
topic | Digital Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10687430/ https://www.ncbi.nlm.nih.gov/pubmed/38034907 http://dx.doi.org/10.3389/fdgth.2023.1279644 |
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