Development of a machine learning prediction model for overactive bladder in female nurses: the NURS study.

purposeTo develop and internally validate interpretable machine-learning models for identifying individuals with a higher probability of overactive bladder (OAB) among female nursing professionals.

methodsA total of 13,191 female nurses participating in the Nurse Urinary Related Health Study were included and divided into training (70%) and testing (30%) cohorts. Seven distinct machine learning algorithms were implemented and evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and F1 score. The best-performing model was interpreted using SHapley Additive exPlanations (SHAP) to identify feature contributions. Recursive feature elimination based on SHAP importance scores was employed to develop a parsimonious model while maintaining optimal predictive performance.

resultsOAB was observed in 11.2% of female nurses. The logistic regression algorithm outperformed all other models, demonstrating the highest predictive performance (validation AUC 0.751; 95% CI 0.729-0.774). Through systematic feature reduction, a streamlined 10-feature model was identified as the final predictive tool. The most influential predictors, ranked by mean SHAP value were: urine holding behavior, straining to void, sleep disorder, delayed voiding, perceived stress, fluid limitation, body mass index (BMI), anxiety, constipation, and parity. A nomogram was constructed to support practical implementation of the model.

conclusionOur study developed a clinically applicable risk assessment tool for estimating OAB probability in female nurses, with conventional logistic regression outperforming more complex machine learning algorithms. The parsimonious model provides a practical screening tool that could be integrated into occupational health programs.