Editorial
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Psychiatry. Feb 19, 2022; 12(2): 204-211
Published online Feb 19, 2022. doi: 10.5498/wjp.v12.i2.204
Screening dementia and predicting high dementia risk groups using machine learning
Haewon Byeon
Haewon Byeon, Department of Medical Big Data, Inje University, Gimhae 50834, South Korea
Author contributions: Byeon H designed the study, involved in data interpretation, preformed the statistical analysis, and assisted with writing the article.
Supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, No. 2018R1D1A1B07041091 and 2021S1A5A8062526.
Conflict-of-interest statement: No benefits in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article.
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: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Haewon Byeon, DSc, PhD, Associate Professor, Director, Department of Medical Big Data, Inje University, 197 Inje-ro, Gimhae 50834, South Korea. bhwpuma@naver.com
Received: June 25, 2021
Peer-review started: June 25, 2021
First decision: September 5, 2021
Revised: September 6, 2021
Accepted: January 19, 2022
Article in press: January 19, 2022
Published online: February 19, 2022
Core Tip

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.