By Lori Solomon

A machine learning model can predict overactive bladder (OAB) risk in women using common reproductive and sociodemographic data, according to a studypublishedonline Aug. 12 inJMIR Medical Informatics.

Guoqiang Huang, M.D., and Shuangquan Lin, Ph.D., M.D., both from the Second Affiliated Hospital of Nanchang University in China, developed and validated a machine learning-based model to predict OAB risk in women, incorporating reproductive and sociodemographic factors. The model was developed using data from 7,884 participants across four consecutive cycles (2011–2018) of the National Health and Nutrition Examination Survey.

The researchers identified five variables as significant predictors. Of the 11 machine learning models, the random forest (RF) showed the highest predictive performance, achieving an area under the receiver operating characteristic curve of 0.8536 in the training set and 0.6999 in the test set, indicating moderate predictive capability. The top three contributors to OAB risk in a Shapley Additive Explanations (SHAP) analysis were age, body mass index (BMI) and number of vaginal deliveries. There was a positive association between age and BMI and OAB risk, and a negative association between the ratio of family income to poverty threshold and OAB risk, in both restricted cubic spline (RCS) and SHAP analyses. RCS also demonstrated higher OAB risk with an earlier age at menarche and a greater number of vaginal deliveries.

"The RF model demonstrated good predictive accuracy and practical clinical applicability, serving as a noninvasive, cost-effective tool for early screening and risk stratification in community health and primary outpatient settings," the authors write.

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More information Guoqiang Huang et al, A Machine Learning–Based Model to Predict Overactive Bladder Risk Among US Women: Evidence From the National Health and Nutrition Examination Survey 2011-2018, JMIR Medical Informatics (2026). DOI: 10.2196/80133