Predicting the occurrence of stress urinary incontinence after prolapse surgery: a machine learning-based model.

Previous models had poor discrimination and calibration among a Chinese population. Hence, we developed and validated an XGBoost model, which performed well irrespective of the preoperative subjective urinary incontinence (preUI) and surgical methods. Further validation is still required.