Table 4.

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

Feature Set	Validation	Accuracy	Sensitivity	Specificity	AUC
FS1	5-fold	81.81 ± 2.13	71.17 ± 3.6	90.96 ± 3.18	0.8106 ± 0.0215
	10-fold	83.75 ± 2.00	72.59 ± 2.25	93.35 ± 2.89	0.8297 ± 0.0197
	Leave-one-out	82.50 ± 𝛜	67.57 ± 𝛜	95.35 ± 𝛜	0.8146 ± 𝛜
FS2	5-fold	75.83 ± 1.72	61.26 ± 2.01	88.37 ± 3	0.7482 ± 0.0166
	10-fold	74.82 ± 2.26	61.39 ± 3.45	86.38± 2.3	0.7389 ± 0.0231
	Leave-one-out	77.50 ± 𝛜	64.86 ± 𝛜	88.37 ± 𝛜	0.7662 ± 𝛜
FS3	5-fold	74.28 ± 1.87	81.46 ± 2.25	68.11 ± 2.97	0.7479 ± 0.0183
	10-fold	74.58 ± 2.00	80.63 ± 3.63	69.38 ± 2.48	0.75 ± 0.0206
	Leave-one-out	77.50 ± 𝛜	86.49 ± 𝛜	69.77 ± 𝛜	0.7813 ± 𝛜
FS4	5-fold	72.50 ± 𝛜	51.35 ± 𝛜	90.70 ± 𝛜	0.7102 ± 𝛜
	10-fold	72.50 ± 𝛜	51.35 ± 𝛜	90.70 ± 𝛜	0.7102 ± 𝛜
	Leave-one-out	72.50 ± 𝛜	51.35 ± 𝛜	90.70 ± 𝛜	0.7102 ± 𝛜
FS5	5-fold	84.37 ± 2.01	75.23 ± 4.25	92.25 ± 2.57	0.8373 ± 0.021
	10-fold	84.50 ± 1.27	76.49 ± 2.72	91.39 ± 2.56	0.8394 ± 0.0127
	Leave-one-out	87.50 ± 𝛜	81.08 ± 𝛜	93.02 ± 𝛜	0.8705 ± 𝛜
FS6	5-fold	85.42 ± 0.93	73.87 ± 1.28	95.35 ± 1.34	0.8461 ± 0.0092
	10-fold	85.94 ± 0.83	74.33 ± 1.36	95.93 ± 1.00	0.8513 ± 0.0084
	Leave-one-out	86.25 ± 𝛜	75.68 ± 𝛜	95.35 ± 𝛜	0.8551 ± 𝛜
FS7	5-fold	85.18 ± 1.04	78.38 ± 1.44	91.03 ± 1.49	0.8471 ± 0.0103
	10-fold	87.63 ± 1.53	80.27 ± 2.11	93.95 ± 1.54	0.8711 ± 0.0155
	Leave-one-out	88.75 ± 𝛜	81.08 ± 𝛜	95.35 ± 𝛜	0.8821 ± 𝛜