Comparison of Ensemble Learning Models for Hypertension Risk Prediction

Amalya Rivana; Abilla Gustien; Farraz Andiko; Nur Amila Adlina; Farras Zidan

doi:10.57152/predatecs.v4i1.2504

Authors

Amalya Rivana Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Abilla Gustien Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Farraz Andiko Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Nur Amila Adlina Universiti Malaysia Terengganu, Malaysia
Farras Zidan Al-Azhar University, Egypt

DOI:

https://doi.org/10.57152/predatecs.v4i1.2504

Keywords:

Decision Tree, Ensemble Learning, Hypertension Risk Prediction, LightGBM, Random Forest, XGBoost

Abstract

Hypertension remains a leading cause of cardiovascular disease worldwide, underscoring the need for accurate early risk prediction. This study aims to (1) compare the predictive performance of four representative machine learning models Decision Tree (DT), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost) for hypertension risk prediction using a large, publicly available clinical dataset, and (2) examine the trade-off between predictive accuracy and model interpretability across these algorithms. The models were evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), along with confusion-matrix-derived accuracy, precision, recall (sensitivity), specificity, and F1-score, under a k-fold cross-validation protocol. Results show that ensemble methods significantly outperform a single Decision Tree: LightGBM achieved the highest AUC (0.9976), followed by XGBoost (0.9966) and Random Forest (0.9923), while the Decision Tree attained an AUC of 0.9189. These results directly address the study objectives, demonstrating that gradient-boosting frameworks capture complex, non-linear relationships within medical data more effectively than a single interpretable tree, while Random Forest offers a favorable balance between accuracy and interpretability. The findings support integrating advanced ensemble models into clinical decision-support systems to enable more reliable, earlier hypertension risk stratification

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