Publication Name	World Journal of Psychiatry
Manuscript ID	69315
Country	South Korea

Received	2021-06-25 08:34
Peer-Review Started	2021-06-25 08:36
To Make the First Decision
Return for Revision	2021-09-05 21:12
Revised	2021-09-06 10:38
Second Decision	2022-01-17 05:42
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2022-01-20 05:22
Articles in Press	2022-01-20 05:22
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2022-02-09 08:11
Publish the Manuscript Online	2022-02-17 08:22

ISSN	2220-3206 (online)
Open Access	This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
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Category	Psychiatry
Manuscript Type	Editorial
Article Title	Screening dementia and predicting high dementia risk groups using machine learning
Manuscript Source	Invited Manuscript
All Author List	Haewon Byeon
ORCID	Author(s) ORCID Number Haewon Byeon http://orcid.org/0000-0002-3363-390X
Funding Agency and Grant Number	Funding Agency Grant Number National Research Foundation of Korea 2018R1D1A1B07041091 National Research Foundation of Korea 2021S1A5A8062526
Corresponding Author	Haewon Byeon, DSc, PhD, Associate Professor, Director, Director, Department of Medical Big Data, Inje University, 197 Inje-ro, Gimhae 50834, South Korea. bhwpuma@naver.com
Key Words	Dementia; Artificial intelligence; Clinical decision support system; Machine learning; Mild cognitive impairment
Core Tip	The predictive performance of machine learning techniques varies among studies because of the difference in machine data (especially, Y variables) imbalance, characteristics of features included in the model, and measurement methods of outcome variables. Therefore, further studies are continuously needed to check the predictive performance of each algorithm because, although some studies have proven that the performance of a specific machine learning algorithm is excellent, the results cannot be generalized for all types of data.
Publish Date	2022-02-17 08:22
Citation	Byeon H. Screening dementia and predicting high dementia risk groups using machine learning. World J Psychiatr 2022; 12(2): 204-211
URL	https://www.wjgnet.com/2220-3206/full/v12/i2/204.htm
DOI	https://dx.doi.org/10.5498/wjp.v12.i2.204

Full Article (PDF)	WJP-12-204.pdf
Full Article (Word)	WJP-12-204.docx
Manuscript File	69315_Auto_Edited.docx
Answering Reviewers	69315-Answering reviewers.pdf
Audio Core Tip	69315-Audio core tip.m4a
Conflict-of-Interest Disclosure Form	69315-Conflict-of-interest statement.pdf
Copyright License Agreement	69315-Copyright license agreement.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	69315-Grant application form(s).pdf
Non-Native Speakers of English Editing Certificate	69315-Language certificate.pdf
Peer-review Report	69315-Peer-review(s).pdf
Scientific Editor Work List	69315-Scientific editor work list.pdf
CrossCheck Report	69315-CrossCheck report.pdf