Clinical decision support for precolonoscopy cancer triage: A rule-out-oriented machine learning model for colorectal cancer risk

Table 2 Discrimination and classification performance at the Youden-optimised threshold (repeated stratified 5-fold × 50-repeat cross-validation; 95% confidence intervals from 2000 bootstrap resamples)

Model	Youden cut-off	ROC-AUC (95%CI)	PR-AUC (95%CI)	Brier score (95%CI)	Sensitivity (95%CI)	Specificity (95%CI)	PPV (95%CI)	NPV (95%CI)
All features	0.0046	0.734 (0.637-0.835)	0.078 (0.022-0.191)	0.0152 (0.0099-0.0208)	0.565 (0.455-1.000)	0.846 (0.388-0.905)	0.051 (0.017-0.086)	0.993 (0.989-1.000)
SHAP10	0.0005	0.736 (0.642-0.826)	0.076 (0.020-0.183)	0.0153 (0.0098-0.0208)	0.913 (0.538-1.000)	0.503 (0.485-0.872)	0.026 (0.018-0.067)	0.997 (0.991-1.000)
SHAP5	0.0025	0.711 (0.597-0.812)	0.055 (0.023-0.152)	0.0154 (0.0101-0.0211)	0.739 (0.400-0.963)	0.629 (0.357-0.944)	0.028 (0.017-0.087)	0.994 (0.989-0.999)

All metrics are computed on out-of-fold predictions pooled across 250 cross-validation folds (5 × 50). The Youden cut-off (maximum sensitivity + specificity - 1) is reported as a statistical reference only; clinically relevant operating points (pt = 0.02-0.05) are discussed in the decision curve analysis. CI: Confidence interval; ROC-AUC: Area under the receiver operating characteristic curve; PR-AUC: Area under the precision-recall curve; PPV: Positive predictive value; NPV: Negative predictive value; SHAP: SHapley Additive exPlanations.