Case Control Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
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 by Basic 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
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:
Corresponding author: Haewon Byeon, DSc, PhD, Professor, Department of Medical Big Data, College of AI Convergence, Inje University, Gimhae 50834, Gyeonsangnamdo, South Korea.
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

Despite the frequent progression from Parkinson’s disease (PD) to Parkinson’s disease dementia (PDD), the basis to diagnose early-onset Parkinson dementia (EOPD) in the early stage is still insufficient.


To explore the prediction accuracy of sociodemographic factors, Parkinson's motor symptoms, Parkinson’s non-motor symptoms, and rapid eye movement sleep disorder for diagnosing EOPD using PD multicenter registry data.


This study analyzed 342 Parkinson patients (66 EOPD patients and 276 PD patients with normal cognition), younger than 65 years. An 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.


The overall accuracy of the random forest was 89.5%, and was higher than that of discriminant analysis (78.3%) and that of the naive Bayesian model (85.8%). In the random forest model, the Korean Mini Mental State Examination (K-MMSE) score, Korean Montreal Cognitive Assessment (K-MoCA), sum of boxes in Clinical Dementia Rating (CDR), global score of CDR, motor score of Untitled Parkinson’s Disease Rating (UPDRS), and Korean Instrumental Activities of Daily Living (K-IADL) score were confirmed as the major variables with high weight for EOPD prediction. Among them, the K-MMSE score was the most important factor in the final model.


It was found that Parkinson-related motor symptoms (e.g., motor score of UPDRS) and instrumental daily performance (e.g., K-IADL score) in addition to cognitive screening indicators (e.g., K-MMSE score and K-MoCA score) were predictors with high accuracy in EOPD prediction.

Keywords: Early-onset Parkinson dementia, Ensemble learning method, Neuropsychological test, Risk factor, Discriminant analysis, Naive Bayesian model

Core Tip: It is believed that if the Korean Mini Mental State Examination (K-MMSE) is given priority over other cognitive screening tests in order to distinguish early-onset Parkinson dementia (EOPD) from Parkinson’s disease, the accuracy of detecting EOPD will be higher than conducting other screening tests first. However, further epidemiological studies will be needed to fully comprehend the results of better accuracy of the K-MMSE than that of Korean Montreal Cognitive Assessment while detecting EOPD using the developed ensemble-based prediction model.