Table 3:

Top performing model for each feature set, along with the 3 other data-preprocessing results using the same modeling strategy (external data)

Feature Set	Processing	Algorithm	Feature Selection	mAUC	LogLoss	Brier Score	Best Model
CE_ET and F_PTR^{^a}	None/none	SVM-P	ICC	0.818	0.929	0.548	False
	SD/none^{^b}	SVM-P	ICC	0.833	0.871	0.521	True
	None/ComBat	SVM-P	ICC	0.808	1.029	0.588	False
	SD/ComBat	SVM-P	ICC	0.817	0.949	0.564	False
CE_ET and T2_PTR^{^a}	None/none	ENET	None	0.808	0.904	0.520	False
	SD/none	ENET	None	0.817	0.867	0.499	False
	None/ComBat^{^b}	ENET	None	0.841	0.922	0.492	True
	SD/ ComBat	ENET	None	0.835	0.891	0.487	False
CE_ET, A_ET and F_PTR^{^a}	None/none	SVM-P	ICC	0.873	0.764	0.433	False
	SD/none^{^b}	SVM-P	ICC	0.886	0.712	0.414	True
	None/ComBat	SVM-P	ICC	0.836	0.872	0.520	False
	SD/ ComBat	SVM-P	ICC	0.873	0.749	0.444	False
CE_ET^{^a}	None/none	SVM-P	ICC	0.819	0.881	0.499	False
	SD/None^{^b}	SVM-P	ICC	0.859	0.789	0.472	True
	None/ComBat	SVM-P	ICC	0.821	0.962	0.531	False
	SD/ ComBat	SVM-P	ICC	0.842	0.850	0.512	False

Note:—ENET indicates multinomial elastic net; SVM-P, support vector machine-polynomial kernel, LogLoss, ??????; A, ADC; F, FLAIR
↵^aRow indicates models using BIP only, but with otherwise the same modeling strategy.
↵^bRow indicates the top-performing model for each feature set.