Table 6.

Comparison of experimental results of classification accuracy (%), sensitivity (%), specificity (%), and AUC (in terms of mean ± standard deviation) using a DT classification model, where 𝛜 indicates 1.0 × 10⁻⁵.

Feature Set	Validation	Accuracy	Sensitivity	Specificity	AUC
FS1	5-fold	75.45 ± 2.86	76.35 ± 4.22	84.71 ± 6.62	0.7553 ± 0.0266
	10-fold	75.50 ± 1.27	77.30 ± 1.32	73.95 ± 1.74	0.7563 ± 0.0125
	Leave-one-out	77.50 ± 𝛜	72.97 ± 𝛜	81.40 ± 𝛜	0.7718 ± 𝛜
FS2	5-fold	70.63 ± 1.88	53.60 ± 4.25	85.27 ± 4.58	0.6944 ± 0.0182
	10-fold	71.00 ± 0.94	54.59 ± 3.15	85.12 ± 3.78	0.6978 ± 0.0082
	Leave-one-out	70.00 ± 𝛜	45.95 ± 𝛜	90.70 ± 𝛜	0.6832 ± 𝛜
FS3	5-fold	66.79 ± 1.13	61.78 ± 4.88	71.10 ± 3.90	0.6644 ± 0.0122
	10-fold	65.00 ± 2.85	62.16 ± 2.96	67.44 ± 3.89	0.6480 ± 0.0280
	Leave-one-out	66.25 ± 𝛜	70.27 ± 𝛜	62.79 ± 𝛜	0.6653 ± 𝛜
FS4	5-fold	66.88 ± 3.59	61.71 ± 2.88	71.32 ± 5.48	0.6652 ± 0.0347
	10-fold	67.50 ± 1.12	58.38 ± 2.76	75.35 ± 1.86	0.6686 ± 0.0117
	Leave-one-out	65.00 ± 𝛜	56.76 ± 𝛜	72.09 ± 𝛜	0.6442 ± 𝛜
FS5	5-fold	78.44 ± 2.32	79.39 ± 3.56	77.62 ± 4.93	0.7851 ± 0.0221
	10-fold	80.25 ± 3.10	79.46 ± 2.16	80.93 ± 5.58	0.8019 ± 0.0293
	Leave-one-out	82.50 ± 𝛜	83.78 ± 𝛜	81.40 ± 𝛜	0.8259 ± 𝛜
FS6	5-fold	79.84 ± 3.09	76.01 ± 4.95	83.14 ± 4.15	0.7958 ± 0.0312
	10-fold	79.82 ± 1.82	79.92 ± 4.30	79.73 ± 3.67	0.7983 ± 0.0185
	Leave-one-out	83.75 ± 𝛜	83.78 ± 𝛜	83.72 ± 𝛜	0.8375 ± 𝛜
FS7	5-fold	81.46 ± 1.97	77.93 ± 2.88	84.50 ± 3.72	0.8121 ± 0.019
	10-fold	80.36 ± 1.10	80.31 ± 2.78	80.40 ± 2.44	0.8035 ± 0.0112
	Leave-one-out	83.75 ± 𝛜	83.78 ± 𝛜	83.72 ± 𝛜	0.8375 ± 𝛜