Next Article in Journal
The Teaching Digital Competence of Health Sciences Teachers. A Study at Andalusian Universities (Spain)
Next Article in Special Issue
Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study
Previous Article in Journal
Impact and Effectiveness of Group Strategies for Supporting Breastfeeding after Birth: A Systematic Review
Previous Article in Special Issue
Frailty as a Moderator of the Relationship between Social Isolation and Health Outcomes in Community-Dwelling Older Adults
Article

Predicting the Severity of Parkinson’s Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression Model

Department of Medical Big Data, College of AI Convergence, Inje University, Gimhae 50834, Gyeonsangnamdo, Korea
Academic Editor: Xudong Huang
Int. J. Environ. Res. Public Health 2021, 18(5), 2551; https://doi.org/10.3390/ijerph18052551
Received: 18 February 2021 / Accepted: 2 March 2021 / Published: 4 March 2021
(This article belongs to the Special Issue Prevention and Management of Frailty)
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. View Full-Text
Keywords: Parkinson’s disease dementia; instrumental activities of daily living; clinical dementia rating; convergence rate; neuropsychological tests; neuropsychiatric symptoms Parkinson’s disease dementia; instrumental activities of daily living; clinical dementia rating; convergence rate; neuropsychological tests; neuropsychiatric symptoms
Show Figures

Figure 1

MDPI and ACS Style

Byeon, H. Predicting the Severity of Parkinson’s Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression Model. Int. J. Environ. Res. Public Health 2021, 18, 2551. https://doi.org/10.3390/ijerph18052551

AMA Style

Byeon H. Predicting the Severity of Parkinson’s Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression Model. International Journal of Environmental Research and Public Health. 2021; 18(5):2551. https://doi.org/10.3390/ijerph18052551

Chicago/Turabian Style

Byeon, Haewon. 2021. "Predicting the Severity of Parkinson’s Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression Model" International Journal of Environmental Research and Public Health 18, no. 5: 2551. https://doi.org/10.3390/ijerph18052551

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop