Identification of key predictors of postmenopausal osteoporosis from routine clinical indicators using explainable machine learning

Background: Machine learning (ML) shows promise in using clinical data to predict chronic diseases. However, its application in PMOP risk assessment using readily available clinical and biochemical parameters is underexplored. Objective: This study aimed to develop and validate an interpretable ML-based model for assessing PMOP using clinical features and laboratory biomarkers, and to identify factors associated with PMOP using SHapley Additive exPlanations (SHAP). Methods: A retrospective cross-sectional study included 1,717 postmenopausal women from two hospitals in Northwest China. PMOP was diagnosed with dual-energy X-ray absorptiometry (DXA T-score ≤−2.5). Data collected included demographics, clinical details, and various laboratory parameters, such as bone metabolism markers, 25-hydroxyvitamin D [25-(OH)D], electrolytes, and routine blood counts. Ten ML algorithms were employed for feature selection and model construction on a dataset split into training (n = 1201) and testing (n = 516) sets. Performance was evaluated using the Area Under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and calibration. Results: The Extra Trees (ET) model achieved the best test-set performance, with an AUC of 0.717 (95% CI: 0.682–0.752). SHAP analysis revealed that age was the most significant associated factor (SHAP value: 0.0648), followed by body mass index (BMI) (0.0243) and chloride ion levels (0.0209). Other top predictors included the use of antihypertensive drugs and years since menopause. Conclusion: The ET ML algorithm showed the best performance in assessing PMOP, with age, BMI, and circulating chloride levels as significant associated factors.