What question did this study set out to answer?

The research aims to develop and evaluate machine learning models for accurately predicting coronary heart disease, especially in resource-limited situations.

January 10, 2026PLoS ONE0 citationsOpen Access

Enhanced machine learning and hybrid ensemble approaches for Coronary Heart Disease prediction

MWMaurice WanyonyiUniversity of Embu ZMZakayo Ndiku MorrisUniversity of Embu FMFaith Mueni MusyokaUniversity of Embu

Key Result

The stacking ensemble model achieved an accuracy of 87.2% and an AUC of 0.94, significantly outperforming traditional classifiers in predicting coronary heart disease.

Key Points

The research aims to develop and evaluate machine learning models for accurately predicting coronary heart disease, especially in resource-limited situations.
Utilized a dataset of 253,680 individuals from the Behavioral Risk Factor Surveillance System.
Employed preprocessing techniques like normalization and the Synthetic Minority Oversampling Technique (SMOTE).
Compared baseline models (Decision Trees, Random Forests, Gradient Boosting, Support Vector Machines) with enhanced versions.
Implemented ensemble approaches including stacking, boosting, and bagging for model evaluation.
PGBM achieved the highest sensitivity at 90.8%.
HIRF demonstrated the best calibration and balance with an AUC of 0.937.
The stacking ensemble model had an accuracy of 87.2%, sensitivity of 89.6%, specificity of 84.7%, and an AUC of 0.94.
Enhanced models outperformed baseline models, showing high accuracy and low overfitting.

Structured PICO

Do enhanced machine learning and hybrid ensemble models improve the prediction of coronary heart disease compared to traditional baseline models?

Population

253,680 individuals from the Behavioral Risk Factor Surveillance System dataset

Intervention

Enhanced machine learning models (Adaptive Noise-Resistant Decision Tree, Hybrid Imbalanced Random Forest, Pruned Gradient Boosting Machine, Enhanced Support Vector Machine) and ensemble approaches (stacking, boosting, bagging, Bayesian model averaging, majority voting)

Comparator

Baseline machine learning models (Decision Trees, Random Forests, Gradient Boosting, and Support Vector Machines)

Outcome

Diagnostic performance measured by accuracy, sensitivity, specificity, and area under the curve (AUC)surrogate

Hybrid ensemble machine learning models, particularly stacking ensembles, significantly outperform traditional classifiers in predicting coronary heart disease, offering a scalable tool for early detection.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

Coronary heart disease (CHD) remains the leading cause of mortality worldwide, disproportionately affecting low- and middle-income countries where diagnostic resources are limited. Traditional statistical models often fail to deliver adequate predictive accuracy in complex, high-dimensional, and imbalanced health datasets. To develop and evaluate enhanced machine learning and hybrid ensemble models for the prediction of coronary heart disease, with a focus on improving diagnostic performance, interpretability, and applicability in resource-constrained settings. We utilized a nationally representative dataset of 253,680 individuals from the Behavioral Risk Factor Surveillance System. Preprocessing included normalization and balancing via the Synthetic Minority Oversampling Technique (SMOTE). Baseline models—Decision Trees, Random Forests, Gradient Boosting, and Support Vector Machines—were compared against improved versions: Adaptive Noise–Resistant Decision Tree (ADNRT), Hybrid Imbalanced Random Forest (HIRF), Pruned Gradient Boosting Machine (PGBM), and Enhanced Support Vector Machine (ESVM). Ensemble approaches (stacking, boosting, bagging, Bayesian model averaging and majority voting) were implemented and evaluated using accuracy, sensitivity, specificity, and area under the curve (AUC). Calibration and learning curves were also analyzed. Enhanced models consistently outperformed their baseline counterparts. PGBM achieved the highest sensitivity (90.8%), while HIRF demonstrated the best overall calibration and balance (AUC = 0.937; sensitivity = 88.4%; specificity = 82.9%). The stacking ensemble emerged as the best-performing model with an accuracy of 87.2%, sensitivity of 89.6%, specificity of 84.7%, and AUC of 0.94. Calibration and learning curve analyses confirmed strong generalizability and low overfitting across ensemble models. Hybrid ensemble machine learning models significantly outperform traditional classifiers in CHD prediction, offering high accuracy, robustness, and interpretability. These models present a scalable framework for implementing AI-driven diagnostic tools in low–resource environments, potentially transforming early detection and prevention of coronary heart disease.

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