Publication Name	World Journal of Psychiatry
Manuscript ID	94298
Country	China

Received	2024-03-15 02:37
Peer-Review Started	2024-03-15 02:37
To Make the First Decision
Return for Revision	2024-04-23 05:18
Revised	2024-05-01 04:27
Second Decision	2024-05-21 02:43
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-05-21 06:44
Articles in Press	2024-05-21 06:44
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-06-03 01:49
Publish the Manuscript Online	2024-06-19 12:40

ISSN	2220-3206 (online)
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Category	Psychiatry
Manuscript Type	Case Control Study
Article Title	Identification of male schizophrenia patients using brain morphology based on machine learning algorithms
Manuscript Source	Unsolicited Manuscript
All Author List	Tao Yu, Wen-Zhi Pei, Chun-Yuan Xu, Chen-Chen Deng and Xu-Lai Zhang
ORCID	Author(s) ORCID Number Tao Yu http://orcid.org/0000-0002-2785-1505 Xu-Lai Zhang http://orcid.org/0009-0004-7865-4108
Funding Agency and Grant Number	Funding Agency Grant Number University Research Fund of Anhui Medical University 2022xkj119
Corresponding Author	Xu-Lai Zhang, MD, PhD, Doctor, Doctor, Psychiatry Department, Hefei Fourth People's Hospital, No. 316 Huangshan Road, Hefei 230032, Anhui Province, China. 479800330@qq.com
Key Words	Schizophrenia; Machine learning; Classification; Structure; Magnetic Resonance imaging
Core Tip	Schizophrenia is a severe psychiatric disease characterized by impairments in cognition, positive and negative symptoms, affecting about 1% of the general population worldwide. A fast diagnosis of schizophrenia is crucial to prescription of an appropriate anti-psychotic in the early stage, which is able to make treatment more efficient. Many studies have demonstrated widespread functional and structural brain alternations from magnetic resonance imaging in individuals with schizophrenia in relation to healthy controls. our aims were to employ four commonly used machine learning algorithms including general linear model, random forest, k-nearest neighbors, and support vector machine and a wider range of brain morphological features to avoid bias towards a particular machine learning algorithm and improve the performance of classification between male individuals with schizophrenia and healthy controls in the present study.
Publish Date	2024-06-19 12:40
Citation	<p>Yu T, Pei WZ, Xu CY, Deng CC, Zhang XL. Identification of male schizophrenia patients using brain morphology based on machine learning algorithms. <i>World J Psychiatry</i> 2024; 14(6): 804-811</p>
URL	https://www.wjgnet.com/2220-3206/full/v14/i6/804.htm
DOI	https://dx.doi.org/10.5498/wjp.v14.i6.804

Full Article (PDF)	WJP-14-804-with-cover.pdf
STROBE Statement	94298-STROBE-statement.pdf
Manuscript File	94298_Auto_Edited-YJP.docx
Answering Reviewers	94298-answering-reviewers.pdf
Audio Core Tip	94298-audio.m4a
Biostatistics Review Certificate	94298-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form	94298-conflict-of-interest-statement.pdf
Copyright License Agreement	94298-copyright-assignment.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	94298-foundation-statement.PDF
Signed Informed Consent Form(s) or Document(s)	94298-informed-consent-statement.pdf
Institutional Review Board Approval Form or Document	94298-institutional-review-board-statement.pdf
Non-Native Speakers of English Editing Certificate	94298-non-native-speakers.pdf
Peer-review Report	94298-peer-reviews.pdf
Scientific Misconduct Check	94298-scientific-misconduct-check.png
Scientific Editor Work List	94298-scientific-editor-work-list.pdf
CrossCheck Report	94298-crosscheck-report.pdf