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

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.

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).

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.

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.

The ET ML algorithm showed the best performance in assessing PMOP, with age, BMI, and circulating chloride levels as significant associated factors.