Abstract
Parkinson's disease (PD) is a neurodegenerative disorder that influence brain's neurological, behavioral, and physiological functions and includes motor and nonmotor manifestations. Although there have been several PD diagnosis systems with supervised machine learning techniques, there are more efforts that need to enhance the accurate detection of PD in its early stage. The current paper developed a novel approach by integrating Least Squares Support Vector Regression (LS-SVR) and Fuzzy Clustering for Unified Parkinson's Disease Rating Scale (UPDRS) diagnosis. This paper used feature selection and Principal Component Analysis (PCA) to overcome the multicollinearity issues in data. This paper used a large medical dataset including Motor- and Total-UPDRS to demonstrate how the proposed method can improve prediction performance via extensive evaluations and comparisons with existing methods. Compared to other prediction methods, the experimental results demonstrate that the proposed method provided the best accuracy for Total-UPDRS (Root Mean Squared Error = 0.7348; R2 = 0.9169) and Motor-UPDRS (Root Mean Squared Error = 0.8321; R2 = 0.8756) predictions.
Original language | English |
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Pages (from-to) | 569-585 |
Number of pages | 17 |
Journal | Biocybernetics and Biomedical Engineering |
Volume | 44 |
Issue number | 3 |
DOIs | |
Publication status | Published - 26 Aug 2024 |
ASJC Scopus subject areas
- Biomedical Engineering
Keywords
- Early diagnosis
- Fuzzy clustering
- Machine learning
- Parkinson disease
- Support vector regression