Table 2.

Performance of each model, with varying usage of features. The values for validation sets are Mean ± Standard deviation from the 3 cross validations.

Models built with all available features
Metric	AUROC	Accuracy	Precision	Recall	F1
RF_val	0.691 ± 0.075	0.671 ± 0.064	0.743 ± 0.042	0.823 ± 0.096	0.777 ± 0.043
RF_test	0.734	0.811	0.828	0.923	0.873
SVM_val	0.735 ± 0.083	0.726 ± 0.066	0.780 ± 0.061	0.854 ± 0.053	0.814 ± 0.044
SVM_test	0.734	0.811	0.828	0.923	0.873
Models built only with hand-crafted image features
RF_val	0.683 ± 0.042	0.646 ± 0.033	0.795 ± 0.033	0.671 ± 0.033	0.727 ± 0.017
RF_test	0.760	0.811	0.852	0.885	0.868
SVM_val	0.691 ± 0.046	0.561 ± 0.037	0.840 ± 0.074	0.471 ± 0.032	0.600 ± 0.015
SVM_test	0.794	0.784	0.909	0.769	0.833
Models built only with clinical features
RF_val	0.693 ± 0.056	0.707 ± 0.025	0.757 ± 0.042	0.875 ± 0.104	0.805 ± 0.026
RF_test	0.636	0.784	0.765	1	0.867
SVM_val	0.701 ± 0.086	0.591 ± 0.084	0.810 ± 0.119	0.567 ± 0.136	0.652 ± 0.094
SVM_test	0.657	0.703	0.800	0.769	0.784
Models built only with deep learning features
RF_val	0.692 ± 0.051	0.720 ± 0.055	0.764 ± 0.070	0.880 ± 0.025	0.815 ± 0.033
RF_test	0.670	0.757	0.793	0.885	0.836
SVM_val	0.726 ± 0.052	0.707 ± 0.068	0.737 ± 0.069	0.912 ± 0.033	0.813 ± 0.044
SVM_test	0.670	0.757	0.793	0.885	0.836