Table 3.

Classification results using significant features

A. Experiment-1: SCZ (n=38) versus HC (n=20)
10-fold method				SVM Model	Hold-out method
ACC (%)	SEN (%)	SPE (%)	AUC (%)		ACC (%)	SEN (%)	SPE (%)	AUC (%)
72.41	86.84	45.00	65.92	Linear SVM	73.68	92.31	33.33	62.82
70.69	78.95	55.00	66.97	Quadratic SVM	78.95††	92.31	50.00	71.15
63.79	68.42	55.00	61.71	Cubic SVM	68.42	76.92	50.00	63.46
70.69	97.37	20.00	58.68	Fine Gaussian SVM	68.42	100.00	0.00	50.00
63.79	92.11	10.00	51.05	Medium Gaussian SVM	73.68	100.00	16.67	58.33
65.52	97.37	5.00	51.18	Coarse Gaussian SVM	68.42	100.00	0.00	50.00
B. Experiment-2: PS (n=18) versus NS (n=10)
10-fold method				SVM Model	Hold-out method
ACC (%)	SEN (%)	SPE (%)	AUC (%)		ACC (%)	SEN (%)	SPE (%)	AUC (%)
85.71	88.89	80.00	84.44	Linear SVM	88.89	100.00	75.00	87.50
82.14	83.33	80.00	81.67	QUADRATIC SVM	88.89	100.00	75.00	87.50
82.14	83.33	80.00	81.67	Cubic SVM	88.89	100.00	75.00	87.50
64.29	100.00	0.00	50.00	Fine Gaussian SVM	55.56	100.00	0.00	50.00
89.29††	100.00	70.00	85.00	Medium Gaussian SVM	77.78	100.00	50.00	75.00
64.29	100.00	0.00	50.00	Coarse Gaussian SVM	55.56	100.00	0.00	50.00

^†† Model showing highest accuracy. SVM – Support vector machine; ACC – Accuracy; SEN – Sensitivity; SPE – Specificity; AUC – Area under receiver operating curve; SCZ –Schizophrenia patients; HC – Healthy controls; PS – Schizophrenia patients with predominant positive symptoms; NS – Schizophrenia patients with predominant negative symptoms