Can machine learning models predict if I will have Stress Urinary Incontinence after giving birth?

Machine learning models can predict your risk of stress urinary incontinence (SUI) after childbirth, and some perform very well. SUI is the leaking of urine when you cough, laugh, sneeze, or exercise. These models use your health history, pregnancy details, and delivery factors to estimate your personal risk. However, the accuracy varies between models, and they are not yet a standard part of care. Your doctor can help you understand what these predictions mean for you.

What the research says

A 2025 study developed and tested machine learning models to predict postpartum SUI using data from 1,208 women. Among five algorithms, the random forest model performed best, with an AUC of 0.995, meaning it could almost perfectly distinguish who would develop SUI. The model used ten factors, including birth weight, weight gain during pregnancy, BMI before delivery, and age at delivery ⁷. Another 2025 study focused on bothersome SUI in parous women in China. The XGBoost model achieved an AUC of 0.720 in validation, which is moderate accuracy. This study included 8,830 women and found that bothersome SUI occurred in 9.6% of them ⁸. A systematic review of seven studies with 4,072 patients found that machine learning models for postpartum SUI show high predictive AUC, but the results were inconsistent across studies. The review noted that methodological quality and risk of bias varied, so the reliability of these models is not fully certain ¹.

What to ask your doctor

• What is my personal risk of developing stress urinary incontinence after delivery?
• Are there any validated prediction tools you use to assess my risk?
• Which factors from my pregnancy and delivery history are most important for my risk?
• If I am at high risk, what preventive measures or treatments are available?
• How accurate are these prediction models, and what are their limitations?