Table 7.

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

Feature Set	Validation	Accuracy	Sensitivity	Specificity	AUC
FS1	5-fold	79.38 ± 0.88	72.97 ± 𝛜	84.88 ± 1.64	0.7893 ± 0.0082
	10-fold	79.75± 0.94	72.97 ± 𝛜	85.58 ± 1.74	0.7928 ± 0.0087
	Leave-one-out	80.00 ± 𝛜	72.97 ± 𝛜	86.05 ± 𝛜	0.7951 ± 𝛜
FS2	5-fold	73.03 ± 1.13	58.69 ± 1.89	85.38 ± 1.05	0.7203 ± 0.0116
	10-fold	72.92 ± 0.59	59.01 ± 1.86	84.89 ± 1.17	0.7195 ± 0.0064
	Leave-one-out	71.25 ± 𝛜	56.76 ± 𝛜	83.72 ± 𝛜	0.7024 ± 𝛜
FS3	5-fold	72.29 ± 0.86	74.33 ± 1.36	70.54 ± 2.19	0.7243 ± 0.0078
	10-fold	71.50 ± 1.22	74.6 ± 1.33	68.84 ± 1.86	0.7172 ± 0.0119
	Leave-one-out	72.50 ± 𝛜	75.68 ± 𝛜	69.77 ± 𝛜	0.7272 ± 𝛜
FS4	5-fold	73.13 ± 0.88	50 ± 1.91	93.02 ± 𝛜	0.7151 ± 0.0095
	10-fold	73.39 ± 0.56	50.58 ± 1.22	93.02 ± 𝛜	0.718 ± 0.0061
	Leave-one-out	73.75 ± 𝛜	51.35 ± 𝛜	93.02 ± 𝛜	0.7219 ± 𝛜
FS5	5-fold	81.56 ± 0.54	73.99 ± 1.31	88.08 ± 0.77	0.8103 ± 0.0057
	10-fold	81.75 ± 0.83	74.87 ± 1.24	87.67 ± 1.06	0.8127 ± 0.0083
	Leave-one-out	82.50 ± 𝛜	75.68 ± 𝛜	88.37 ± 𝛜	0.8202 ± 𝛜
FS6	5-fold	82.92 ± 0.59	73.42 ± 1.01	91.09 ± 0.86	0.8226 ± 0.0059
	10-fold	82.32 ± 0.8	72.97 ± 2.04	90.37 ± 0.82	0.8167 ± 0.0087
	Leave-one-out	82.50 ± 𝛜	72.97 ± 𝛜	90.70 ± 𝛜	0.8184 ± 𝛜
FS7	5-fold	83.00 ± 0.93	75.68 ± 𝛜	89.30 ± 1.54	0.8249 ± 0.0077
	10-fold	82.29 ± 0.47	75.68 ± 𝛜	87.98 ± 0.87	0.8183 ± 0.0043
	Leave-one-out	82.50 ± 𝛜	75.68 ± 𝛜	88.37 ± 𝛜	0.8202 ± 𝛜