中国实用妇科与产科杂志2025，Vol.41Issue(7)：734-737,741,5.DOI:10.19538/j.fk2025070114
应用机器学习建立产后女性压力性尿失禁风险预测模型
Application of machine learning to establish a risk prediction model for postpartum women with stress urinary incontinence
摘要
Abstract
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.关键词
产后女性压力性尿失禁/机器学习/随机森林/模型Key words
postpartum stress urinary incontinence in women/machine learning/random forest/model分类
医药卫生引用本文复制引用
杨晴晴,朱珏,汪期明,杨名洁,钱苗红,张晶..应用机器学习建立产后女性压力性尿失禁风险预测模型[J].中国实用妇科与产科杂志,2025,41(7):734-737,741,5.基金项目
Key Projects of Ningbo Public Welfare Science and Technology Plan(2022S034) （2022S034）
Ningbo Top Medical and Health Research Program(2024021020) （2024021020）
General Program of Medicine and Health in Zhejiang Province(2020KY878) 宁波市公益类科技计划重点项目(2022S034) （2020KY878）
宁波市医疗卫生高端团队(2024021020) （2024021020）
浙江省医药卫生面上项目(2020KY878) （2020KY878）
