What is the clinical evidence from this study?

Study design: Observational. Population: Hypertension (n=612). Intervention: Stacking ensemble model vs. Single machine learning models. Primary outcome: Hypertension prediction accuracy.

December 23, 2024PLoS ONE11 citationsOpen Access

Optimizing hypertension prediction using ensemble learning approaches

ISIsteaq Kabir SifatHajee Mohammad Danesh Science and Technology University MKMd. Kaderi KibriaHajee Mohammad Danesh Science and Technology University

Key Result

A stacking ensemble machine learning model effectively predicted hypertension risk, achieving an accuracy of 96.32% and an AUC of 0.971.

Study Design

Type

Observational (n=612)

Structured PICO

Does a stacking ensemble machine learning model improve the accuracy of hypertension prediction compared to single-model methods in participants from Ethiopia?

Population

612 participants from Ethiopia evaluated for hypertension risk using machine learning models.

Exposure

Stacking ensemble machine learning model using 13 selected features

Comparator

Single-model machine learning methods (logistic regression, artificial neural network, random forest, extreme gradient boosting, light gradient boosting machine)

Outcome

Predictive performance for hypertension (accuracy, precision, recall, F1-score, and AUC)

A stacking ensemble machine learning model demonstrated high accuracy and robustness for predicting hypertension risk, identifying weight, drinking habits, and prior hypertension as key risk factors.

Abstract

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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