Table 2.

Classification performance and predictive values of the machine learning model for the detection of coronary artery disease (CAD) in the validation, holdout, and external test datasets.

Dataset	Total no.	CAD control, no. (%)	CAD case, no. (%)	AUROC (95% CI)	Sensitivity (95% CI)	Specificity (95% CI)	Accuracy (95% CI)	NPV (95% CI)	PPV (95% CI)
Validation dataset	20,497	17,828 (87)	2,669 (13)	0.95 (0.94 – 0.95)	0.94 (0.94 – 0.95)	0.82 (0.81 – 0.83)	0.88 (0.87 – 0.89)	0.93 (0.93 – 0.94)	0.84 (0.83 – 0.85)
Holdout dataset	15,252	12,791 (84)	2,461 (16)	0.93 (0.92 – 0.93)	0.90 (0.89 –0.90)	0.88 (0.87 – 0.88)	0.89 (0.89 – 0.89)	0.89 (0.89 –0.89)	0.88 (0.88 –0.88)
External test dataset	60,186	52,058 (86)	8,128 (14)	0.91 (0.91 – 0.91)	0.84 (0.83 – 0.84)	0.83 (0.82 – 0.83)	0.85 (0.85 – 0.85)	0.84 (0.83 – 0.84)	0.83 (0.82 – 0.83)

No., number; AUROC, area under the receiver-operating-characteristic curve; NPV, negative predictive value; PPV, positive predictive value.