Table 4.

Performances of the ML Model for Predicting FFR ≤0.80

	AUC	Sensitivity	Specificity	PPV	NPV	Overall Accuracy
Using all 28 features
Training set (5‐fold CV)a	0.84±0.03	0.78±0.04	0.78±0.05	0.77±0.05	0.79±0.05	0.78±0.04
Test set	0.86	0.82	0.79	0.79	0.82	0.80
External validation cohort	0.90	0.72	0.89	0.75	0.87	0.84
Using the 12 selected features
Training set	0.86	0.79	0.80	0.77	0.82	0.79
Test set	0.87	0.84	0.80	0.81	0.84	0.82
External validation cohort	0.87	0.80	0.87	0.74	0.90	0.85
By 2000 bootstrap iterations
Training setb	0.87±0.01 (0.86–0.88)	0.81±0.01 (0.79–0.83)	0.77±0.01 (0.74–0.79)	0.75±0.01 (0.73–0.76)	0.83±0.01 (0.81–0.84)	0.79±0.01 (0.77–0.80)
Test setb	0.87±0.01 (0.86–0.87)	0.84±0.02 (0.81–0.87)	0.77±0.01 (0.75–0.80)	0.78±0.01 (0.76–0.80)	0.83±0.01 (0.81–0.86)	0.81±0.01 (0.79–0.82)

AUC indicates area under curve; CV, cross‐validation; FFR, fractional flow reserve; ML, machine learning; NPV, negative predictive value; PPV, positive predictive value.

^aMean±SD with 5‐fold CV.

^bAveraged performances of 2000 bootstrap replicates as mean±SD, (bootstrap CIs).