Model may help identify women at risk for overactive bladder

A machine learning model developed by researchers from the Second Affiliated Hospital of Nanchang University in China can predict overactive bladder (OAB) risk in women using common reproductive and sociodemographic data, according to a study published in JMIR Medical Informatics. The model, validated using data from 7,884 participants in the National Health and Nutrition Examination Survey, identified age, body mass index (BMI), and number of vaginal deliveries as significant predictors of OAB risk.

The random forest (RF) model, which achieved an area under the receiver operating characteristic curve of 0.8536 in the training set and 0.6999 in the test set, demonstrated good predictive accuracy and practical clinical applicability as a noninvasive, cost-effective tool for early screening and risk stratification in community health and primary outpatient settings.

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