PMID- 33806474
OWN - NLM
STAT- MEDLINE
DCOM- 20210423
LR  - 20231111
IS  - 1660-4601 (Electronic)
IS  - 1661-7827 (Print)
IS  - 1660-4601 (Linking)
VI  - 18
IP  - 5
DP  - 2021 Mar 4
TI  - Predicting the Severity of Parkinson's Disease Dementia by Assessing the 
      Neuropsychiatric Symptoms with an SVM Regression Model.
LID - 10.3390/ijerph18052551 [doi]
LID - 2551
AB  - In this study, we measured the convergence rate using the mean-squared error 
      (MSE) of the standardized neuropsychological test to determine the severity of 
      Parkinson's disease dementia (PDD), which is based on support vector machine 
      (SVM) regression (SVR) and present baseline data in order to develop a model to 
      predict the severity of PDD. We analyzed 328 individuals with PDD who were 60 
      years or older. To identify the SVR with the best prediction power, we compared 
      the classification performance (convergence rate) of eight SVR models (Eps-SVR 
      and Nu-SVR with four kernel functions (a radial basis function (RBF), linear 
      algorithm, polynomial algorithm, and sigmoid)). Among the eight models, the MSE 
      of Nu-SVR-RBF was the lowest (0.078), with the highest convergence rate, whereas 
      the MSE of Eps-SVR-sigmoid was 0.110, with the lowest convergence rate. The 
      results of this study imply that this approach could be useful for measuring the 
      severity of dementia by comprehensively examining axial atypical features, the 
      Korean instrumental activities of daily living (K-IADL), changes in rapid eye 
      movement sleep behavior disorder (RBD), etc. for optimal intervention and caring 
      of the elderly living alone or patients with PDD residing in medically vulnerable 
      areas.
FAU - Byeon, Haewon
AU  - Byeon H
AUID- ORCID: 0000-0002-3363-390X
AD  - Department of Medical Big Data, College of AI Convergence, Inje University, 
      Gimhae 50834, Gyeonsangnamdo, Korea.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20210304
PL  - Switzerland
TA  - Int J Environ Res Public Health
JT  - International journal of environmental research and public health
JID - 101238455
SB  - IM
MH  - Activities of Daily Living
MH  - Aged
MH  - Algorithms
MH  - *Alzheimer Disease
MH  - Humans
MH  - Neuropsychological Tests
MH  - *Parkinson Disease/diagnosis/epidemiology
MH  - Support Vector Machine
PMC - PMC7967659
OTO - NOTNLM
OT  - Parkinson's disease dementia
OT  - clinical dementia rating
OT  - convergence rate
OT  - instrumental activities of daily living
OT  - neuropsychiatric symptoms
OT  - neuropsychological tests
COIS- The author declares no conflict of interest.
EDAT- 2021/04/04 06:00
MHDA- 2021/04/24 06:00
PMCR- 2021/03/04
CRDT- 2021/04/03 01:23
PHST- 2021/02/18 00:00 [received]
PHST- 2021/03/02 00:00 [accepted]
PHST- 2021/04/03 01:23 [entrez]
PHST- 2021/04/04 06:00 [pubmed]
PHST- 2021/04/24 06:00 [medline]
PHST- 2021/03/04 00:00 [pmc-release]
AID - ijerph18052551 [pii]
AID - ijerph-18-02551 [pii]
AID - 10.3390/ijerph18052551 [doi]
PST - epublish
SO  - Int J Environ Res Public Health. 2021 Mar 4;18(5):2551. doi: 
      10.3390/ijerph18052551.