TABLE 6.

Classification performance metrics of spectral feature with three different cross-validation approaches using five classifiers.

Classifier	Approach
	Leave-one-sample-out				Leave-one-fold-out				Five-fold-stratified-cross-validation
	ACC	SENS	SPEC	F1	ACC	SENS	SPEC	F1	ACC	SENS	SPEC	F1
SVM	94.73	92.86	100.00	96.29	88.18	87.60	94.00	92.95	95.00	93.33	100.00	96.00
ANN	73.68	100.00	0.00	84.85	90.91	100.00	0.00	95.24	73.33	100.00	0.00	84.57
RF	89.47	92.85	80.00	92.85	87.27	87.00	90.00	92.39	95.00	93.33	100.00	96.00
EDT	94.73	92.85	100.00	96.29	61.45	64.00	36.00	60.95	90.00	93.33	80.00	93.14
AdaBoost	94.73	100.00	80.00	96.55	9.00	0.00	100.00	0.00	90.00	100.00	60.00	94.28

Using ACC and F1 as comparable metrics across three cross-validation approaches, SVM and RF classifiers using the proposed spectral features consistently perform well.

Metrics: Accuracy (ACC), Sensitivity (SENS), Specificity (SPEC), and F1-score (F1).