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

doi:10.5498/wjp.v14.i6.804

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Jun 19, 2024 (publication date) through Jun 29, 2024

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World Journal of Psychiatry

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2220-3206

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Psychiatry. Jun 19, 2024; 14(6): 804-811
Published online Jun 19, 2024. doi: 10.5498/wjp.v14.i6.804

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

Citation: Yu T, Pei WZ, Xu CY, Deng CC, Zhang XL. Identification of male schizophrenia patients using brain morphology based on machine learning algorithms. World J Psychiatry 2024; 14(6): 804-811
URL: https://www.wjgnet.com/2220-3206/full/v14/i6/804.htm
DOI: https://dx.doi.org/10.5498/wjp.v14.i6.804