Peng Bohao, Luo Yu, Wei Chengcheng, Su Shuai, Song Liangdong
Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Transl Androl Urol. 2025 Aug 30;14(8):2302-2314. doi: 10.21037/tau-2025-282. Epub 2025 Aug 26.
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.
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.
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.
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.
膀胱过度活动症（OAB）是一种对日常生活有严重影响的泌尿系统综合征。目前，评估OAB风险的方法相对有限，主要依赖患者自身报告的症状。迫切需要开发新的OAB诊断风险模型。本研究旨在通过训练机器学习（ML）模型来评估女性人群中OAB的风险。
基于2007年至2018年的美国国家健康与营养检查调查（NHANES）数据，共有10807名女性参与者被纳入模型。使用支持向量机（SVM）、逻辑回归拟合、K近邻（KNN）、随机森林（RF）算法、梯度提升、决策树（DT）、极端梯度提升（XGBoost）来开发OAB风险模型。模型构建中使用了10个特征因素。
在七种ML算法中，RF模型表现最佳，曲线下面积（AUC）值为0.879。在10个特征因素中，高血压是最重要的影响因素，糖尿病和睡眠障碍对OAB风险的影响也不容忽视。
结果表明，本研究中通过ML技术构建的女性OAB风险模型具有良好的诊断性能和可解释性，有助于提高女性人群中OAB的诊断水平。
