Development and external validation of models to improve prediction of osteoporosis in elderly women: interpretable machine learning

A random forest model using readily available clinical data predicts osteoporosis risk in older women with robust internal and external performance. The deployed model outputs calibrated probabilities at the patient level, provides case level explanations using SHAP, and supports dynamic rescoring as new routine results become available, enabling individualized risk management in routine care.