Table 3.

Comprehensive summary of model performance

Method	Dataset	AUC (95% CI)	Specificity	Sensitivity	Intercept of calibration curve*	Slope of calibration curve
Bayesian Network	Training Set	0.849 (0.839–0.859)	0.875	0.762	< 0.0001	0.99
	Testing Set	0.821 (0.803–0.840)	0.896	0.724	< 0.0001	0.94
	Validation Set	0.800 (0.785–0.815)	0.882	0.690	0.0003	1.48
	Sensitivity Analysis (30% missing)	0.791 (0.777–0.806)	0.849	0.681	0.0005	1.51
Logistic Regression Model	Training Set	0.838 (0.829–0.847)	0.899	0.734	0.0002	1.16
	Testing Set	0.832 (0.817–0.847)	0.899	0.734	0.0002	1.1
	Validation Set	0.799 (0.787–0.811)	0.903	0.664	0.0001	2.26
	Sensitivity Analysis (30% missing)	0.746 (0.733–0.759)	0.867	0.603	0.0013	0.18

* Represents the absolute value of the intercept of calibration curves