Identification of male schizophrenia patients using brain morphology based on machine learning algorithms

Table 1 Comparison of age and whole volume between two groups

Variables	Healthy controls	Schizophrenia patients	t value	P value
Age (yr)	34.06 ± 8.88	35.04 ± 11.21	-0.338	0.736
Whole volume (mm3)	1203600.00 ± 88093.08	1164100.00 ± 112071.00	1.365	0.176

Table 2 The important features extracted by least absolute shrinkage and selection operator

Variables		Coefficients
Surface area	Bankssts	-0.00005308106
	Inferior temporal	-0.00004908730
	Lateral occipital	-0.00003610912
	Lingual	-0.00022333510
	Insula	0.000007207081
	Isthmus cingulate	0.000156094500
	Paracentral	0.000248385300
Gray matter volume	Superior frontal	-0.00001763374
	Temporal pole	0.000149290400
Cortical thickness	Lingual	0.062200590000
	Cuneus	0.037246120000
	Lateral occipital	0.239341600000
	Par sopercularis	0.000000005200

Table 3 Comparison of morphological features between two groups

Variables	Schizophrenia patients	Healthy controls	t value	P value
Left bankssts area	1016.20 ± 170.13	1133.20 ± 186.00	-2.488	0.015
Left inferior temporal area	3492.30 ± 557.45	3856.30 ± 423.39	-2.544	0.013
Left lateral occipital area	4925.30 ± 719.19	5489.80 ± 480.09	-3.111	0.003
Left lingual area	2812.00 ± 444.30	3236.70 ± 479.05	-3.471	0.001
Left lingual thickness	2.08 ± 0.17	1.99 ± 0.10	2.121	0.037
Left superior frontal volume	23123.00 ± 2824.16	24744.00 ± 2448.14	-2.187	0.032
Right cuneus thickness	2.01 ± 0.14	1.89 ± 0.14	2.938	0.004
Right lateral occipital thickness	2.18 ± 0.15	2.07 ± 0.15	2.738	0.008

Table 4 Performance of each machine learning algorithm

Algorithms	AUC	Balanced accuracy, %	Sensitivity	Specificity
GLM	0.728 (0.470-0.986)	61.84	0.737	0.500
RF	0.886 (0.754-1.000)	64.04	0.947	0.333
KNN	0.601 (0.257-0.945)	55.70	0.947	0.167
SVM	0.842 (0.670-1.000)	50.00	1.000	0.000

AUC: Area under receiver operating characteristic curves; GLM: General linear model; RF: Random forest; KNN: K-nearest neighbor; SVM: Support vector machine.

Table 5 The performance for each structural feature

Features	AUC	Balanced accuracy, %	Sensitivity	Specificity
Surface area	0.474 (0.241-0.706)	39.50	0.789	0.000
Gray matter volume	0.553 (0.235-0.871)	56.60	0.632	0.500
Cortical thickness	0.605 (0.327-0.884)	55.70	0.947	0.167

AUC: Area under receiver operating characteristic curves.