Table 4.

(a) Results of univariate ROC analysis: Areas under ROC curve (AUCs) are shown. (b) Best results of multivariate SVM analysis using either linear or sigmoid function as its kernel: AUCs estimated from leave-one-out cross-validation trials are shown. (a-b) Best AUC results in each comparison are highlighted in bold.

(a) Univariate ROC analysis: AUC results
	Bone	ALC vs CHOW	ALC vs PF	PF vs CHOW
P7	Skull	0.558	0.666	0.67
	Frontal	0.522	0.5	0.512
	Parietal	0.54	0.512	0.56
	Occipital	0.549	0.571	0.502
	Mandible	0.507	0.505	0.503
P21	Skull	0.852	0.922	0.558
	Frontal	0.801	0.746	0.616
	Parietal	0.902	0.92	0.527
	Occipital	0.864	0.838	0.663
	Mandible	0.694	0.692	0.528
	Nasal	0.708	0.587	0.604
P21-P7	Skull	0.914	0.817	0.678
	Frontal	0.794	0.661	0.613
	Parietal	0.885	0.917	0.589
	Occipital	0.811	0.783	0.593
	Mandible	0.621	0.65	0.561

(b) Multivariate SVM analysis using linear or sigmoid function as kernel: Best AUC results
	Kernel Function	ALC vs CHOW		ALC vs PF		PF vs CHOW
		Bones*	AUC	Bones*	AUC	Bones*	AUC
P7	Linear	P,M	0.629	F,M,S	0.751	P,S	0.805
	Sigmoid	F,O	0.616	O,S	0.621	M,S	0.742
P21	Linear	P,M	0.889	P,M,S	0.934	F,P,O	0.688
	Sigmoid	M,O	0.912	P,M,S	0.995	P,O,S	0.732
P21-P7	Linear	F,M,S	0.918	P,M	0.917	P,M,S	0.759
	Sigmoid	O,S	0.967	M,P	0.928	O,P	0.761

^*Bones used in the SVM model: Selected from Frontal (F), Parietal (P), Occipital (O), Mandible (M), and Skull (S).