Table 3.

The performance of the models in the training and test datasets

Model	Accuracy			AUC (95%CI)			Sensitivity			Specificity			PPV			NPV
	Train	Test		Train	Test		Train	Test		Train	Test		Train	Test		Train	Test
LAD-Model	0.662	0.649		0.679 (0.609, 0.748)	0.664 (0.517, 0.811)		0.699	0.785		0.625	0.517		0.653	0.611		0.673	0.714
LCX-Model	0.636	0.614		0.651 (0.580, 0.723)	0.623 (0.474, 0.773)		0.664	0.643		0.607	0.586		0.630	0.600		0.642	0.630
RCA-Model	0.662	0.684		0.706 (0.638, 0.773)	0.675 (0.531, 0.820)		0.584	0.571		0.741	0.793		0.695	0.727		0.639	0.657
PCAT-Model	0.702	0.649		0.764 (0.703, 0.825)	0.723 (0.589, 0.857)		0.797	0.714		0.607	0.586		0.672	0.625		0.747	0.680
Cli-Model	0.702	0.667		0.752 (0.689, 0.815)	0.706 (0.564, 0.847)		0.460	0.464		0.946	0.862		0.897	0.765		0.635	0.625
Overall Model	0.760	0.719		0.828 (0.776, 0.881)	0.797 (0.679, 0.915)		0.770	0.643		0.750	0.793		0.757	0.750		0.764	0.697

LAD left anterior descending artery, LCX left circumflex coronary artery, RCA right coronary artery, AUC area under the curve, CI confidence interval, PPV positive predict value, NPV negative predict value