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

As populations age and the prevalence of osteoporosis (OP) increases, osteoporotic fractures substantially raise disability and mortality and impose growing economic burdens, threatening health and quality of life. This study aimed to develop and externally validate a reliable, practical machine learning model to predict OP in older women using routine clinical test results and comorbidity data.

We retrospectively assembled an internal dataset from NHANES (2003-2020) and randomly split it 70:30 into training and test sets. An external cohort from a Chinese tertiary hospital was used for validation. Predictors were selected using LASSO in the training set. Five algorithms (XGBoost, SVM, RF, LightGBM, and Naive Bayes) were tuned, and model performance was evaluated on the test set and in the external cohort. Calibration curves and decision curve analysis (DCA) were used to assess calibration and clinical net benefit. Feature contributions were quantified with Shapley additive explanations (SHAP).

Among 3,950 women in the internal dataset, 833 (21.1%) had OP; in the external cohort (n=338), 167 (49.4%) had OP. SHAP ranked predictors (high to low) as: age, drinking, diabetes, eGFR, HbA1c, BMI, HDL, TG, BUN, and TBIL. After hyperparameter tuning, RF achieved an AUC of 0.805 in the internal test set and 0.740 in the external cohort; in the internal test set, accuracy was 0.82, precision 0.83, and specificity 0.97. Calibration was acceptable, and DCA indicated clinical utility across relevant thresholds.

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.