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

Table 4 Likelihood ratios and diagnostic odds ratios at the Youden-optimised threshold (95% confidence intervals from 2000 bootstrap resamples)

Model	Threshold	Sensitivity (95%CI)	Specificity (95%CI)	LR+ (95%CI)	LR- (95%CI)	DOR
All features	0.0046	0.565 (0.364-0.783)	0.846 (0.828-0.863)	3.66 (2.34-5.19)	0.514 (0.257-0.752)	7.1
SHAP10	0.0005	0.913 (0.778-1.000)	0.503 (0.476-0.528)	1.84 (1.55-2.07)	0.173 (0.000-0.445)	10.6
SHAP5	0.0025	0.739 (0.538-0.909)	0.629 (0.605-0.652)	1.99 (1.45-2.46)	0.415 (0.149-0.735)	4.8

Likelihood ratios are prevalence-independent and therefore provide evidence that the model’s performance is not simply a mathematical consequence of the low malignancy prevalence (1.43%). The SHAP10 negative likelihood ratio of 0.173 indicates that a negative model result reduces the post-test odds of malignancy by approximately 5.8-fold, consistent with a rule-out-oriented triage role. CI: Confidence interval; LR+: Positive likelihood ratio = sensitivity/(1 - specificity); LR-: Negative likelihood ratio = (1 - sensitivity)/specificity; DOR: Diagnostic odds ratio. SHAP: SHapley Additive exPlanations.