Population-based colorectal cancer risk prediction using a SHAP-enhanced LightGBM model.

Background: Colorectal cancer (CRC) is a highly frequent cancer worldwide, and early detection and risk stratification playing a critical role in reducing both incidence and mortality. we aimed to develop and validate a machine learning (ML) model using clinical data to improve CRC identification and prognostic evaluation. Methods: We analyzed multicenter datasets comprising 676 CRC patients and 410 controls from Guigang City People's Hospital (2020-2024) for model training/internal validation, with 463 patients from Laibin City People's Hospital for external validation. Seven ML algorithms were systematically compared, with Light Gradient Boosting Machine (LightGBM) ultimately selected as the optimal framework. Model performance was rigorously assessed through area under the receiver operating characteristic (AUROC) analysis, calibration curves, Brier scores, and decision curve analysis. SHAP (SHapley Additive exPlanations) methodology was employed for feature interpretation. Results: The LightGBM model demonstrated exceptional discrimination with AUROCs of 0.9931 (95% CI: 0.9883-0.998) in the training cohort and 0.9429 (95% CI: 0.9176-0.9682) in external validation. Calibration curves revealed strong prediction-actual outcome concordance (Brier score=0.139). SHAP analysis identified 13 key predictors, with age (mean SHAP value=0.216) and CA19-9 (mean SHAP value=0.198) as dominant contributors. Other significant variables included hematological parameters (WBC, RBC, HGB, PLT), biochemical markers (ALT, TP, ALB, UREA, uric acid), and gender. A clinically implementable web-based risk calculator was successfully developed for real-time probability estimation. Conclusions: Our LightGBM-based model achieves high predictive accuracy while maintaining clinical interpretability, effectively bridging the gap between complex ML systems and practical clinical decision-making. The identified biomarker panel provides biological insights into CRC pathogenesis. This tool shows significant potential for optimizing early diagnosis and personalized risk assessment in CRC management.