目的 探讨应用机器学习构建产后女性压力性尿失禁（SUI）风险预测模型并内部验证，为早期防治提供依据。方法 纳入2022年10月至2023年6月宁波大学附属妇女儿童医院516例产后42d盆底筛查产妇（SUI病例组82例，对照组434例），提取年龄、分娩方式、产次、盆底肌电生理数据、盆底超声指标等影响因素，采用随机森林、支持向量机等5种机器学习算法建模，通过十折交叉验证比较模型效能。结果 产后SUI发生率15.89%（82/516）。随机森林模型表现最优，训练集F1值1.000、AUC 1.000，验证集F1值0.776、AUC 0.837。关键影响因素包括盆底肌电生理参数（紧张收缩、耐力收缩等阶段）、年龄、产次、会阴损伤、体重指数（BMI）等，其中盆底肌电参数对分类贡献最高。结论 随机森林模型预测效能最佳，盆底肌电生理参数与产后 SUI 强相关。该模型可辅助医务人员精准评估风险，指导早期个性化康复干预，如凯格尔运动、生物反馈治疗等，为产后SUI防治提供新工具。

Objective To develop and internally validate a risk prediction model for postpartum stress urinary incontinence (SUI) in women using machine learning, aiming to provide evidence for early prevention. Methods This retrospective study included 516 postpartum women who underwent pelvic floor screening at 42 days after delivery at Ningbo University Women and Children's Hospital from October 2022 to June 2023 (a case group of 82 cases and a control group of 434 cases). The influencing factors were collected, including age, delivery mode, parity, electromyographic(EMG) data of pelvic floor and pelvic floor ultrasound data. Five machine learning algorithms such as random forest (RF) and support vector machine (SVM) were used to build predictive models. Model performance was evaluated using 10-fold cross-validation. Results The overall incidence of postpartum SUI was 15.89%. Among all models, the random forest model demonstrated the best performance, with an F1 score of 1.000 and AUC of 1.000 in the training set, and an F1 score of 0.776 and AUC of 0.837 in the validation set. The key influencing factors included pelvic floor EMG parameters (e.g., intense contraction and endurance contraction phases), age, parity, perineal injury, and BMI. Pelvic floor EMG parameters contributed the most to classification performance. Conclusions The random forest model shows the best predictive ability. Pelvic floor EMG parameters are strongly associated with postpartum SUI. This model offers a valuable tool for clinicians to accurately assess individual risk and guide early rehabilitation intervention, such as Kegel exercises and biofeedback therapy, which provides new tools for the prevention and treatment of postpartum SUI.