Table 2. Comparison of different models by outcome.

Outcome	Model	AUC (95% CI)	Accuracy	Sensitivity	Specificity	NRI
Hemorrhea	LR	0.781 (0.733–0.829)	0.715	0.671	0.759	0
	RF	0.852 (0.817–0.887)	0.752	0.750	0.754	0.710
	DNN	0.882 (0.810–0.954)	0.803	0.773	0.833	0.101
	XGBoost	0.921 (0.864–0.978)	0.845	0.851	0.837	0.140
Cardiac death	LR	0.791 (0.722–0.860)	0.806	0.878	0.538	0
	RF	0.884 (0.874–0.894)	0.783	0.808	0.684	0.093
	DNN	0.906 (0.855–0.957)	0.913	0.945	0.789	0.115
	XGBoost	0.939 (0.903–0.975)	0.914	0.950	0.800	0.148
In-stent restenosis	LR	0.838 (0.792–0.884)	0.750	0.699	0.814	0
	RF	0.863 (0.804–0.922)	0.779	0.646	0.939	0.025
	DNN	0.887 (0.829–0.945)	0.801	0.778	0.829	0.049
	XGBoost	0.915 (0.863–0.967)	0.834	0.778	0.902	0.077

AUC, area under the curve; CI, confidence interval; NRI, net reclassification index; LR, logistic regression; RF, random forest, DNN, deep learning neural network; XGBoost, eXtreme Gradient Boost.