Comparison of AdaBoost and Random Forest Methods in Osteoporosis Risk Prediction Based on Machine Learning
DOI:
https://doi.org/10.52436/1.jutif.2026.7.1.5297Keywords:
AdaBoost, Ensemble methods, Machine learning, Osteoporosis prediction, Random forest, Risk classificationAbstract
This study aims to determine the most effective ensemble machine learning algorithm for osteoporosis risk prediction in resource-constrained healthcare settings, specifically comparing AdaBoost and Random Forest performance on Southeast Asian population data. We implemented nested 5-fold cross-validation on a dataset of 1,958 records with 15 lifestyle and demographic attributes. Both algorithms underwent hyperparameter optimization, and performance was evaluated using accuracy, precision, recall, F1-score, and clinical utility metrics including cost-effectiveness analysis. AdaBoost achieved superior performance with 86.90% accuracy (95% CI: 84.2-89.6%) and perfect precision (1.00) compared to Random Forest's 84.69% accuracy and 0.92 precision. Statistical significance testing confirmed AdaBoost's advantage (p=0.032). Clinical implementation in three health centers demonstrated 60% reduction in unnecessary referrals. This is the first study to compare these algorithms specifically for Southeast Asian populations with clinical validation and cost-effectiveness analysis, providing a ready-to-deploy model for resource-limited healthcare settings.
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Copyright (c) 2026 Edwardo Parlindungan H, Setiawan Assegaff, Jasmir Jasmir
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