PMID- 33269221
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20201230
IS  - 2220-3206 (Print)
IS  - 2220-3206 (Electronic)
IS  - 2220-3206 (Linking)
VI  - 10
IP  - 11
DP  - 2020 Nov 19
TI  - Best early-onset Parkinson dementia predictor using ensemble learning among 
      Parkinson's symptoms, rapid eye movement sleep disorder, and neuropsychological 
      profile.
PG  - 245-259
LID - 10.5498/wjp.v10.i11.245 [doi]
AB  - BACKGROUND: 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. AIM: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
CI  - (c)The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights 
      reserved.
FAU - Byeon, Haewon
AU  - Byeon H
AD  - Department of Medical Big Data, College of AI Convergence, Inje University, 
      Gimhae 50834, Gyeonsangnamdo, South Korea. bhwpuma@naver.com.
LA  - eng
PT  - Journal Article
DEP - 20201119
PL  - United States
TA  - World J Psychiatry
JT  - World journal of psychiatry
JID - 101610480
PMC - PMC7672787
OTO - NOTNLM
OT  - Discriminant analysis
OT  - Early-onset Parkinson dementia
OT  - Ensemble learning method
OT  - Naive Bayesian model
OT  - Neuropsychological test
OT  - Risk factor
COIS- 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.
EDAT- 2020/12/04 06:00
MHDA- 2020/12/04 06:01
PMCR- 2020/11/19
CRDT- 2020/12/03 05:47
PHST- 2020/07/21 00:00 [received]
PHST- 2020/09/27 00:00 [revised]
PHST- 2020/10/11 00:00 [accepted]
PHST- 2020/12/03 05:47 [entrez]
PHST- 2020/12/04 06:00 [pubmed]
PHST- 2020/12/04 06:01 [medline]
PHST- 2020/11/19 00:00 [pmc-release]
AID - 10.5498/wjp.v10.i11.245 [doi]
PST - epublish
SO  - World J Psychiatry. 2020 Nov 19;10(11):245-259. doi: 10.5498/wjp.v10.i11.245. 
      eCollection 2020 Nov 19.