Development and validation of a risk prediction model for postoperative urinary retention after gynecologic abdominal-pelvic surgery.

ABSTRACT: This study developed prediction models using 12 machine learning (ML) algorithms and, through comprehensive evaluation, selected the optimal model to predict postoperative urinary retention after gynecologic abdominal-pelvic surgery. An online calculator was created to provide reference for clinical decision-making and future research. The study included 1264 patients, with 436 from a temporal-spatial cohort. LASSO was used to select predictive factors, and SMOTENC oversampling and algorithm tuning were applied. The models were evaluated using AUC, accuracy, F1 score, specificity, recall, and DCA. The results showed that 161 out of 1264 patients (12.73%) developed urinary retention postoperatively. LASSO regression identified 8 key predictive factors: age, menopausal status, surgical metho

SUBMITTER: Shi J 

PROVIDER: S-EPMC12827410 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature