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

Table 3 Calibration performance before and after post-hoc Platt scaling (Cox 1958 framework; fitted on out-of-fold predictions)

Model	Uncalibrated (raw XGBoost output)			After Platt recalibration
	Intercept (95%CI)	Slope (95%CI)	Brier score	Intercept	Slope	Brier score
All features	+0.654 (+0.174 to +1.133)	0.329 (0.171-0.487)	0.0152	0.000	1.003	0.0140
SHAP10	+0.363 (-0.102 to +0.827)	0.315 (0.157-0.472)	0.0153	0.000	1.006	0.0140
SHAP5	-0.042 (-0.493 to +0.409)	0.314 (0.147-0.480)	0.0154	-0.001	1.006	0.0140

The calibration intercept (“calibration-in-the-large”) was obtained as the intercept of a binomial generalised linear model fitted on the observed outcome with the out-of-fold logit predictions as offset; an intercept of 0 indicates no systematic bias. The calibration slope was obtained as the coefficient of a binomial GLM regressing the outcome on the out-of-fold logit; a slope of 1 indicates well-calibrated predictions, whereas values below 1 indicate over-dispersed (too-extreme) predicted probabilities. Platt scaling was applied as a single logistic regression on the out-of-fold logits and restored near-ideal calibration (intercept ≈ 0, slope ≈ 1) for all three models without loss of discrimination, reducing the Brier score uniformly to 0.0140. CI: Confidence interval; XGBoost: Extreme gradient boosting; SHAP: SHapley Additive exPlanations.