Key result
Stacking ensemble machine learning model predicts hypertension risk with ~96% accuracy, outperforming single models.
Why the study?
Hypertension prediction is critical for preventive healthcare, motivating an investigation into whether ensemble learning techniques can improve prediction model accuracy over traditional single-model methods.
Does a stacking ensemble machine learning model improve the accuracy of hypertension risk prediction compared to single-model methods?
Cross-Sectional (n=612)
No
Does a stacking ensemble machine learning model improve the accuracy of hypertension risk prediction compared to single-model methods?
Effect estimate: Accuracy 96.32%
Absolute Event Rate: 96.32% vs 92.61%
Ensemble machine learning approaches significantly improve the accuracy of hypertension risk prediction based on clinical and demographic data, highlighting their potential as a supportive tool for early detection.
Ensemble ML may aid hypertension risk prediction in limited settings; hypothesis-generating pending prospective validation before clinical use.
Hypertension (HTN) prediction is critical for effective preventive healthcare strategies. This study investigates how well ensemble learning techniques work to increase the accuracy of HTN prediction models. Utilizing a dataset of 612 participants from Ethiopia, which includes 27 features potentially associated with HTN risk, we aimed to enhance predictive performance over traditional single-model methods. A multi-faceted feature selection approach was employed, incorporating Boruta, Lasso Regression, Forward and Backward Selection, and Random Forest feature importance, and found 13 common features that were considered for prediction. Five machine learning (ML) models such as logistic regression (LR), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and a stacking ensemble model were trained using selected features to predict HTN. The models' performance on the testing set was evaluated using accuracy, precision, recall, F1-score, and area under the curve (AUC). Additionally, SHapley Additive exPlanations (SHAP) was utilized to examine the impact of individual features on the models' predictions and identify the most important risk factors for HTN. The stacking ensemble model emerged as the most effective approach for predicting HTN risk, achieving an accuracy of 96.32%, precision of 95.48%, recall of 97.51%, F1-score of 96.48%, and an AUC of 0.971. SHAP analysis of the stacking model identified weight, drinking habits, history of hypertension, salt intake, age, diabetes, BMI, and fat intake as the most significant and interpretable risk factors for HTN. Our results demonstrate significant advancements in predictive accuracy and robustness, highlighting the potential of ensemble learning as a pivotal tool in healthcare analytics. This research contributes to ongoing efforts to optimize HTN prediction models, ultimately supporting early intervention and personalized healthcare management.
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Sifat et al. (2024) conducted a cross-sectional in Hypertension (n=612). Stacking ensemble machine learning model vs. Single machine learning models (LR, ANN, RF, XGB, LGBM) was evaluated on Hypertension prediction accuracy (Accuracy 96.32%). The stacking ensemble machine learning model achieved an accuracy of 96.32% and an AUC of 0.971 in predicting hypertension risk, outperforming single-model methods.
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