A female overactive bladder risk model developed by machine learning: based on 2007-2018 NHANES data.

Background: Overactive bladder (OAB) is a urinary system syndrome that has a serious impact on daily life. Currently, the methods for estimating the risk of OAB are relatively limited, mainly relying on the symptoms reported by patients themselves. There is an urgent need to develop new risk models for the OAB diagnosis. This study aims to assess the risk of OAB in the female population by training machine learning (ML) models. Methods: Based on the National Health and Nutrition Examination Survey (NHANES) data from 2007 to 2018, a total of 10,807 female participants were included in the model. Support vector machine (SVM), logistic regression fitting, K-nearest neighbor (KNN), random forest (RF) algorithm, gradient boosting, decision tree (DT), extreme gradient boosting (XGBoost) were used to develop OAB risk models. Ten characteristic factors were used in the construction of the models. Results: Among the seven ML algorithms, the RF model demonstrated the best performance with an area under the curve (AUC) value of 0.879. Among the 10 characteristic factors, hypertension was the most important influencing factor, and the impact of diabetes and sleep disorders on OAB risk cannot be ignored. Conclusions: The results show that the female OAB risk model constructed by ML technology in this study has good diagnostic performance and interpretability, which is helpful to improve the diagnosis of OAB in the female population.