Table 2.

Comparison among machine learning and deep learning algorithms.

Model	Accuracy	Balanced accuracy	AUC (95% CI)
Deep learning	0.97	0.98	0.98 (0.97–0.99)
AdaBoost	0.94	0.61	0.95 (0.93–0.96)
Support vector machine	0.93	0.60	0.92 (0.89–0.94)
K-nearest neighbors	0.89	0.50	0.91 (0.88–0.93)
Extreme Gradient Boosting	0.95	0.54	0.90 (0.86–0.93)
Decision tree	0.79	0.53	0.78 (0.72–0.83)
Logistic regression	0.91	0.59	0.59 (0.51–0.67)
Random forest	0.93	0.52	0.50 (0.41–0.58)