Construction and clinical validation of a machine learning-based risk prediction model for poor wound healing after anorectal surgery.

Objective: To construct and validate a machine learning-based risk prediction model for poor wound healing after anorectal surgery, enabling early identification and individualized intervention. Methods: Clinical data from 246 patients (Jan 2022-Dec 2024) were retrospectively collected. Wound healing status at 4 weeks post-surgery defined outcome (good: Results: BMI, diabetes, stool consistency, and preoperative perianal infection were common core predictors. In internal validation, AUCs were 0.893 (Logistic), 0.897 (RF), and 0.877 (GBM). In temporal validation, AUCs were 0.874 (Logistic), 0.865 (GBM), and 0.853 (RF). Logistic regression showed the smallest AUC fluctuation across training, internal, and temporal sets (0.864→0.893→0.874) and lowest Brier scores (0.092-0.123), indicating best calibration and generalizability. GBM achieved the highest training AUC (0.925) but significant performance decay in validation ( Conclusion: Logistic regression demonstrated the best overall discriminative ability, calibration, clinical net benefit, and cross-cohort generalizability, and is recommended as the preferred tool for individualized risk assessment. RF, after thorough optimization, shows promise as an alternative. GBM was limited by sample size and exhibited overfitting, warranting further validation in larger studies. This study provides a quantitative framework for early risk stratification in anorectal surgery patients.