Skip to main content

CORRECTION

Int J Public Health, 18 May 2023

Corrigendum: Using Machine Learning to Predict Cognitive Impairment Among Middle-Aged and Older Chinese: A Longitudinal Study

Haihong Liu,Haihong Liu1,2Xiaolei Zhang,Xiaolei Zhang3,4Haining Liu,,
Haining Liu2,5,6*Sheau Tsuey Chong,
Sheau Tsuey Chong1,7*
  • 1Centre for Research in Psychology and Human Well-being, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Bangi, Malaysia
  • 2Department of Psychology, Chengde Medical University, Chengde, China
  • 3Department of Biomedical Engineering, Chengde Medical University, Chengde, China
  • 4Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia
  • 5Hebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde, China
  • 6Hebei International Research Center of Medical Engineering, Chengde Medical University, Chengde, China
  • 7Counselling Psychology Programme, Secretariat of Postgraduate Studies, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Bangi, Malaysia

A Corrigendum on
Using Machine Learning to Predict Cognitive Impairment Among Middle-Aged and Older Chinese: A Longitudinal Study

by Liu H, Zhang X, Liu H and Chong ST (2023). Int J Public Health 68:1605322. doi: 10.3389/ijph.2023.1605322

There was an error in the Funding statement. The authors erroneously included the funders “Humanities and Social Science Research Project of Hebei Education Department (SQ2022059), and University-Level Scientific Research Project in CDMC (202113).”

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The first published version of the article has been updated.

Keywords: longitudinal study, machine learning, random forest, middle-aged and older Chinese, cognitive impairment, dementia

Citation: Liu H, Zhang X, Liu H and Chong ST (2023) Corrigendum: Using Machine Learning to Predict Cognitive Impairment Among Middle-Aged and Older Chinese: A Longitudinal Study. Int J Public Health 68:1606127. doi: 10.3389/ijph.2023.1606127

Received: 25 April 2023; Accepted: 09 May 2023;
Published: 18 May 2023.

Copyright © 2023 Liu, Zhang, Liu and Chong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Haining Liu, bGl1aG4wNDAxQHNpbmEuY29t; Sheau Tsuey Chong, c3RjaG9uZ0B1a20uZWR1Lm15

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.