A Machine Learning-Based Model to Predict Overactive Bladder Risk Among US Women: Evidence From the National Health and Nutrition Examination Survey 2011-2018.

Background: Overactive bladder (OAB) is a prevalent condition, particularly among women, characterized by urinary urgency, often accompanied by frequency and nocturia. Traditional risk prediction methods for OAB are limited, as they fail to fully integrate multidimensional risk factors, including female reproductive history. Machine learning offers potential for enhanced predictive accuracy by using large-scale datasets like the National Health and Nutrition Examination Survey (NHANES). Objective: This study aimed to develop and validate a machine learning-based model to predict OAB risk in women, incorporating reproductive and sociodemographic factors, and to identify key predictors using interpretable methods. Methods: This retrospective observational study analyzed data from 7884 participants across 4 consecutive cycles (2011-2018) of the National Health and Nutrition Examination Survey. LASSO (least absolute shrinkage and selection operator) regression and univariate and multivariate logistic regression analyses were applied to identify key variables in the training set. Fourteen variables were selected via LASSO regression for model construction, among which age, BMI, ratio of family income to poverty threshold (PIR), age at menarche, and number of vaginal deliveries were identified as the most significant clinical predictors. The SHAP (Shapley Additive Explanations) method interpreted the optimal model, and restricted cubic spline (RCS) curves were used for dose-response analysis. Results: Five variables were identified as significant predictors. Among the 11 ML models, random forest (RF) demonstrated the highest predictive performance. The random forest model achieved an AUROC (area under the receiver operating characteristic curve) of 0.8536 (95% CI 0.8435-0.8638) in the training set and 0.6999 (95% CI 0.6768-0.7212) in the test set, indicating moderate predictive capability. SHAP analysis identified age, BMI, and the number of vaginal deliveries as the top 3 contributors to OAB risk. Both RCS and SHAP analyses revealed a positive association of age and BMI with OAB risk and a negative association with PIR. Additionally, RCS showed that the risk of OAB was higher with an earlier age at menarche and a greater number of vaginal deliveries. Conclusions: Integrating ML with SHAP interpretability provides a robust predictive tool for OAB, facilitating early identification and clinical management.