The soft voting ensemble machine learning model predicted essential hypertension complicated by coronary heart disease with an AUC of 0.906 and accuracy of 88.8%, outperforming individual classifiers including logistic regression (AUC 0.783, accuracy 70.6%).
Does a soft voting ensemble machine learning model improve the prediction of coronary heart disease risk in patients with essential hypertension compared to individual classifiers?
A soft voting ensemble machine learning model using routine electronic medical record data provides high accuracy (AUC 0.906) for early risk stratification of coronary heart disease in hypertensive patients.
Effect estimate: AUC 0.906 vs. 0.783 (95% CI 0.895-0.918 for soft voting model)
Absolute Event Rate: 0.906% vs 0.783%
Objectives: This study aimed to develop an optimized ensemble learning model to improve the prediction of hypertension complicated by coronary heart disease (CHD) through advanced feature selection and classifier fusion, thereby enhancing both accuracy and stability in risk assessment.Methods: We constructed an ensemble-based predictive model using voting fusion to enhance early detection of hypertension complicated by CHD. The dataset comprised 2,487 patients with essential hypertension (EH) complicated by CHD and 3,904 non-CHD controls. Following data preprocessing procedures, including data cleaning and univariate and multivariate feature selection, an 18-dimensional feature set was derived. Five machine learning algorithms (logistic regression, random forest, XGBoost, CatBoost, and CART) were trained independently and subsequently integrated through a voting ensemble to optimize predictive performance.Results: The voting fusion model outperformed all individual classifiers, achieving an area under the curve of 0.906 and an accuracy of 0.888 in predicting EH complicated by CHD.Conclusions: The proposed ensemble model improves classification accuracy and robustness, offering a clinically useful tool for early risk stratification of hypertension-associated CHD. Although the model demonstrates strong predictive performance using cross-sectional data, its reliance on single-timepoint measurements and selected control populations necessitates further validation. Pending additional studies, this framework may serve as a supplementary decision-support tool within clinical informatics systems.
Hassan et al. (2026) studied Essential hypertension complicated by coronary heart disease (n=6,391). Soft voting ensemble machine learning model combining random forest, XGBoost, CatBoost, CART, and logistic regression vs. Individual machine learning classifiers (random forest, XGBoost, CatBoost, CART, and logistic regression) and hard voting ensemble was evaluated on Prediction accuracy of essential hypertension complicated by coronary heart disease measured by area under the curve (AUC) and accuracy (ACC) (AUC 0.906 vs. 0.783, 95% CI 0.895-0.918 for soft voting model). The soft voting ensemble machine learning model predicted essential hypertension complicated by coronary heart disease with an AUC of 0.906 and accuracy of 88.8%, outperforming individual classifiers including logistic regression (AUC 0.783, accuracy 70.6%).