Early diagnosis of Parkinson’s disease using a hybrid method of least squares support vector regression and fuzzy clustering

• Autorzy: Ahmadi Hossein , Huo Lin , Arji Goli , Sheikhtaheri Abbas , Zhou Shang-Ming

Identyfikatory

DOI

10.1016/j.bbe.2024.08.009

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

early diagnosis; Parkinson disease; support vector regression; fuzzy clustering; machine learning

PL

wczesna diagnoza; choroba Parkinsona; regresja wektora wsparcia; grupowanie rozmyte; uczenie maszynowe

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2024
• Tom: Vol. 44, no. 3
• Strony: 569--585
• Opis fizyczny: Bibliogr. 145 poz., rys., tab., wykr.

Twórcy

autor

Ahmadi Hossein

• Centre for Health Technology, Faculty of Health, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK

autor

Huo Lin

• The International College, Guangxi University, Nanning 530000 China

autor

Arji Goli

• Health Information Management, School of Nursing and Midwifery, Saveh University of Medical Sciences, Iran

autor

Sheikhtaheri Abbas

• Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran

autor

Zhou Shang-Ming

• Centre for Health Technology, Faculty of Health, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).