Interpretable multi-strategy learning for predictive therapeutic adherence to osteoporosis treatment among chronic patients

Abstract

This study presents an interpretable multi-strategy learning framework to predict thera- peutic adherence to osteoporosis treatment among chronic patients, advancing the use of Artificial Intelligence (AI) in adherence prediction. Traditional, ensemble, deep learning, and hybrid models were developed and evaluated using Explainable AI (XAI) techniques LIME, SHAP, and Permutation Feature Importance to identify key adherence factors. The Extreme Gradient Boosting (XGBoost) model achieved the best overall valida- tion performance with 68.3% accuracy, 66.3% F1 score, and 73.4% AUC-ROC and was deployed as a web-based prediction tool on Render for real-time adherence prediction. XAI results showed that lifestyle factors like smoking, low physical activity, and inad- equate calcium intake negatively affected adherence, while regular exercise and sufficient vitamin D intake improved it. These findings highlight an interpretable, data-driven pathway for clinicians and researchers to support personalized interventions and enhance adherence outcomes in osteoporosis management. Deep Learning, Explainable Artifi- cial Intelligence (XAI), Feature Importance, LIME, Machine Learning Interpretability, Osteoporosis, Postmenopausal, SHAP, Therapeutic Adherence, XGBoost