Stratified Procedural Risk Assessment in Colorectal Surgery: A Comparative Analysis of Statistical and Machine Learning Approaches Using Combined Surgical Approach and Operative Duration Categories

Background: Postoperative complications following colorectal surgery remain a persistent clinical challenge. Traditional risk stratification has focused on patient characteristics, while conventional modeling approaches treat procedural factors such as operative duration and surgical approach as independent predictors, potentially obscuring interaction effects. Methods: This study developed a machine learning model stratifying 7908 colorectal surgery patients into four distinct procedural risk categories based on combined surgical approach and operative duration (laparoscopic-short, laparoscopic-long, open-short, open-long), rather than treating these factors as separate variables. A gradient boosting ensemble classifier with RUSBoost resampling was trained on predictor variables including patient demographics, comorbidities, and intraoperative factors. Results: Feature importance analysis revealed that the open-long category emerged as the single most important predictor, substantially exceeding all other variables. Weight loss, body mass index, patient age, and electrolyte abnormalities ranked as the next most important predictors. Stratified complication rates demonstrated a critical interaction: prolonged duration more than doubled complication risk in open procedures (short-duration: 9.99%, long-duration: 20.46%), whereas laparoscopic procedures showed only a modest increase from short-duration (10.45%) to long-duration (14.08%) cases. Logistic regression benchmark analysis confirmed the duration-approach interaction (OR = 1.53, 95% CI: 0.97–2.39), achieving comparable discrimination (c-statistic 0.678 vs. 0.665 for the ensemble model). Decision curve analysis demonstrated logistic regression provided superior clinical utility across most threshold probabilities. Conclusions: The dual analytical framework (i.e., statistical inference for quantifying associations and machine learning for predictive feature ranking) offers complementary insights for clinical application. These findings demonstrate that stratified feature engineering can elucidate complex risk phenotypes that may be obscured when procedural factors are analyzed independently.