Key result
The HDBN-XG algorithm achieved 99% accuracy in predicting coronary heart disease, outperforming existing classifiers such as SVM, MLP, RF, and KNN.
Why the study?
The global rise in heart disease necessitates precise prediction tools to assess individual risk levels.
Does the HDBN-XG algorithm improve coronary heart disease prediction accuracy compared to existing machine learning classifiers?
Does the HDBN-XG algorithm improve coronary heart disease prediction accuracy compared to existing machine learning classifiers?
Absolute Event Rate: 99% vs 62%
The proposed HDBN-XG algorithm demonstrates high accuracy and performance metrics for predicting coronary heart disease, offering a robust data-driven approach for risk assessment.
May enhance research CHD prediction; leaves open prospective clinical validation before any practice change.
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.
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Kalita et al. (2023) studied Coronary Heart Disease (n=462). HDBN-XG algorithm vs. SVM, KNN, MLP, and RF classifiers was evaluated on Prediction accuracy. The HDBN-XG algorithm achieved 99% accuracy in predicting coronary heart disease, outperforming existing classifiers such as SVM, MLP, RF, and KNN.
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