Integrating reproductive and metabolic factors for uterine fibroid risk assessment: a two-center machine learning study with SHAP interpretability

Background Uterine fibroids are common benign gynecological tumors that adversely affect reproductive health. This study aimed to develop a machine learning–based model to predict individualized fibroid risk in women of reproductive age. Methods Six clinical predictors encompassing reproductive and metabolic factors were analyzed. Feature selection was performed using LASSO regression, and multiple machine learning algorithms with cross-validation were compared. The optimal model was assessed for discrimination, calibration, and clinical benefit, with SHapley Additive exPlanations (SHAP) analysis employed to enhance interpretability. Results A total of 1,274 women were included, of whom 762 (59.8%) had uterine fibroids. Six predictors were retained, and among the classifiers tested, the Random Forest achieved the best validation performance (AUC = 0.734) with balanced accuracy and F1 score. SHAP interpretation further identified age, BMI, menarche age, parity, triglycerides, and fasting glucose as the most influential risk factors. Conclusion An interpretable Random Forest model was established to predict uterine fibroid risk, enabling individualized risk stratification and supporting timely preventive interventions in clinical practice.