Supplementary file 1_Development of multi-algorithm machine learning models integrating novel serum biomarkers for survival prediction in colorectal cancer: a retrospective cohort study.docx

This study aims to employ a multi-algorithm collaborative strategy to identify critical prognostic factors for CRC patients and develop integrated prognostic nomograms that combine multi-dimensional features.</p>Methods<p>Deep machine learning analysis was conducted in the R environment using the randomForest, glmnet (for LASSO regression), and caret packages.

Three algorithms (LASSO regression, XGBoost, Random Forest) were applied to screen 73 clinicopathological features, with key hub features identified as the intersection of results from the three methods. Survival analysis was performed using the Kaplan-Meier method (log-rank test), and Cox regression analysis confirmed independent prognostic associations. Nomogram performance was validated via calibration curves, time-dependent AUC, C-index, decision curve analysis (DCA), and internal validation cohorts.</p>Results<p>The multi-algorithm intersection identified 6 key hub features encompassing pathological (M stage, N stage), serological (Cystatin C, Homocysteine, γ-Glutamyl Transferase), and demographic (age) factors.