Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Fuzzy logic has gained substantial attention in PD diagnosis research. PD detection using fuzzy logic has presented more precise outcomes as compared with common machine learning approaches. In this research, a hybrid method combining supervised learning, unsupervised learning and feature selection techniques is developed. In a type-1 fuzzy system, the membership functions used for the fuzzification of the crisp inputs are mapped to single numbers. However, in a type-2 fuzzy system, these numbers are represented as intervals, adding an extra dimension to the definition of the membership function. The first step of the proposed method involves clustering the data using the Expectation–Maximization (EM) technique. The performance of EM clustering is performed using the Davies–Bouldin index. Subsequently, feature selection is performed using the backward stepwise regression. To predict the UPDRS, Type-2 Sugeno fuzzy inference system (T2SFIS) is implemented on the clusters generated from the previous steps. The Parkinson's telemonitoring dataset is used in this study for method evaluation. Using the EM algorithm, the PD dataset was clustered into 13 segments, and the most important features for accurate UPDRS prediction were chosen in each segment using backward stepwise regression. The hybrid method was evaluated using R-squared (R2) and RMSE. The evaluation results showed that the combination of EM, backward stepwise regression, and type-2 Sugeno FIS obtained the best accuracy in predicting Motor-UPDRS and Total-UPDRS.

Publication Date

2024-01-01

Publication Title

International Journal of Fuzzy Systems

Volume

26

Issue

4

ISSN

1562-2479

Acceptance Date

2023-12-05

Deposit Date

2025-10-20

Embargo Period

2025-02-19

Funding

The authors are thankful to the Deanship of Scientific Research under the supervision of the Scientific and Engineering Research Center (SERC) at Najran University for funding this work under the research centers funding program grant code NU/RCP/SERC/12/6. This research is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R4), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Keywords

Backward stepwise regression, Expectation–maximization, Parkinson’s disease, Type-2 Sugeno fuzzy inference system

First Page

1261

Last Page

1284

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