Table 2.

Comparison of models for prevalent CVD detection

		Training and testing sample					Validation sample
Input	Model (coefficients)	Deviance	Deviance ratio	Number of predictors	ROC AUC (95% CI)	P-value	Deviance	Deviance ratio	ROC AUC (95% CI)	P-value
Clinical + VCG	Adaptive lasso, penalized	0.616	0.108	17	0.737 (0.709–0.765)	0.267	0.669	0.101	0.740 (0.683–0.796)	0.928/0.014a
	Lasso, penalized	0.618	0.106	22	0.737 (0.709–0.764)		0.669	0.102	0.740 (0.683–0.796)
	Elastic net, penalized	0.618	0.106	23	0.737 (0.710–0.765)		0.670	0.103	0.741 (0.684–0.798)
	Ridge, penalized	0.617	0.107	43	0.739 (0.712–0.767)		0.668	0.104	0.743 (0.686–0.800)
	Logistic regression	0.608	0.120	42	0.748 (0.721–0.776)		0.670	0.100	0.737 (0.681–0.792)
	Plugin lasso, postselection	0.640	0.073	2	0.707 (0.678–0.737)	0.0008	0.696	0.065	0.687 (0.625–0.749)	0.394
	CNN	—	—	—	0.778 (0.746–0.809)	0.008	—	—	0.660 (0.597–0.722)
	Random Forests	—	—	—	—	—	—	—	0.512 (0–493–0.530)	<0.0001
Clinical + VCG + ECG	Adaptive lasso, penalized	0.555	0.197	47	0.800 (0.775–0.825)	<0.0001b	0.670	0.100	0.732 (0.671–0.792)	0.732b
	Lasso, penalized	0.578	0.163	54	0.786 (0.760–0.812)	<0.0001b	0.665	0.107	0.736 (0.676–0.795)	0.821b
	Elastic net, penalized	0.576	0.167	79	0.792 (0.767–0.818)	<0.0001b	0.664	0.108	0.742 (0.683–0.800)	0.959b
	Plugin lasso, postselection	0.618	0.106	5	0.733 (0.705–0.761)	0.0002b	0.695	0.067	0.676 (0.613–0.738)	0.440b
	CNN	—	—	—	0.664 (0.631–0.697)	<0.0001b	—	—	0.549 (0.478–0.620)	0.020b

^aIn comparison to the convolutional neural network (CNN) and plugin-based lasso models.

^bIn comparison to corresponding VCG model.