Byeon H. Best early-onset Parkinson dementia predictor using ensemble learning among Parkinson's symptoms, rapid eye movement sleep disorder, and neuropsychological profile. World J Psychiatr 2020; 10(11): 245-259 [PMID: 33269221 DOI: 10.5498/wjp.v10.i11.245]
Corresponding Author of This Article
Haewon Byeon, DSc, PhD, Professor, Department of Medical Big Data, College of AI Convergence, Inje University, Gimhae 50834, Gyeonsangnamdo, South Korea. bhwpuma@naver.com
Research Domain of This Article
Psychiatry
Article-Type of This Article
Case Control Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
World J Psychiatr. Nov 19, 2020; 10(11): 245-259 Published online Nov 19, 2020. doi: 10.5498/wjp.v10.i11.245
Best early-onset Parkinson dementia predictor using ensemble learning among Parkinson's symptoms, rapid eye movement sleep disorder, and neuropsychological profile
Haewon Byeon
Haewon Byeon, Department of Medical Big Data, College of AI Convergence, Inje University, Gimhae 50834, Gyeonsangnamdo, South Korea
Author contributions: Byeon H designed the paper, was involved in study data interpretation, preformed the statistical analysis, and assisted with writing the article.
Supported byBasic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, No. NRF-2018R1D1A1B07041091 and NRF-2019S1A5A8034211.
Institutional review board statement: The study was reviewed and approved by the National Biobank of Korea Institutional Review Board, Approval No. KBN-2019-005.
Informed consent statement: All patients gave informed consent.
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.
Data sharing statement: Technical appendix, statistical code, and dataset available from the corresponding author at bhwpuma@naver.com.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
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, Professor, Department of Medical Big Data, College of AI Convergence, Inje University, Gimhae 50834, Gyeonsangnamdo, South Korea. bhwpuma@naver.com
Received: July 21, 2020 Peer-review started: July 21, 2020 First decision: September 17, 2020 Revised: September 27, 2020 Accepted: October 11, 2020 Article in press: October 11, 2020 Published online: November 19, 2020 Processing time: 118 Days and 9.1 Hours
ARTICLE HIGHLIGHTS
Research background
Despite the frequent progression from Parkinson’s disease (PD) to Parkinson dementia, the basis to diagnose early-onset Parkinson dementia (EOPD) in the early stage is still insufficient.
Research motivation
It is limited to develop a highly-reliable model to predict EOPD using individual indicators such as PD symptoms and neuropsychological tests. In order to develop an accurate prediction model, it is necessary to develop a comprehensive model that includes sociodemographic indices, Parkinson’s motor symptoms, Parkinson’s non-motor symptoms, rapid eye movement (REM) sleep behavior disorder, and neuropsychological indices.
Research objectives
The objectives of our study were to explore the prediction accuracy of sociodemographic factors, Parkinson’s motor symptoms, Parkinson’s non-motor symptoms, and REM sleep disorder for diagnosing EOPD using PD multicenter registry data.
Research methods
This study was performed by analyzing the Parkinson’s Disease Epidemiology multicenter registry data provided by the National Biobank of Korea. This study analyzed 342 Parkinson patients (66 EOPD patients and 276 PD patients with normal cognition, younger than 65 years). The EOPD prediction model was developed using a random forest algorithm and the accuracy of the developed model was compared with the naive Bayesian model and discriminant analysis.
Research results
When the factors of EOPD were compared using “normalized importance of variables”, the Korean Mini Mental State Examination score was the most important factor of EOPD. Also, the accuracy of random decision forest was higher than that of naive Bayesian model and that of discriminant analysis.
Research conclusions
It is believed that using random forest will increase accuracy while exploring major variables allowing us to predict EOPD, compared to traditional statistical techniques such as discriminant analysis.
Research perspectives
It is necessary to develop a customized screening test that can early detect EOPD using biomarkers or genetic big data.
