ArticleInternational journal of environmental research and public health2021
Predicting the Severity of Parkinson's Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression Model.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- The Role of Machine Learning in Cognitive Impairment in Parkinson Disease: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Clinical efficacy of donepezil combined with nerve growth factor in the treatment of patients with Parkinson's disease dementia and its impact on adiponectin and soluble tumor necrosis factor-alpha receptor-1.Pakistan journal of medical sciences · 2025Article
- Article
- Using AI-Based Technologies to Help Nurses Detect Behavioral Disorders: Narrative Literature Review.JMIR nursing · 2024Review
- Treadmill training in Parkinson's disease is underpinned by the interregional connectivity in cortical-subcortical network.NPJ Parkinson's disease · 2022Article
- Vibration-Based Approach to Measure Rail Stress: Modeling and First Field Test.Sensors (Basel, Switzerland) · 2022Article
- Can the prediction model using regression with optimal scale improve the power to predict the Parkinson's dementia?World journal of psychiatry · 2022Article
- Explainable artificial intelligence based on feature optimization for age at onset prediction of spinocerebellar ataxia type 3.Frontiers in neuroinformatics · 2022Article
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1 author at 1 institution in 1 country.
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Abstract
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.
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