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Accuracy Analysis of Type-2 Fuzzy System in Predicting Parkinson’s Disease Using Biomedical Voice Measures

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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.

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Data Availability

The data are available in the UCI machine learning archive (https://archive.ics.uci.edu/dataset/189/parkinsons+telemonitoring), which was published online in 2009.

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Acknowledgment

This research is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R4), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

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.

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Authors and Affiliations

  1. UCSI Graduate Business School, UCSI University, 56000, Cheras, Kuala Lumpur, Malaysia

    Mehrbakhsh Nilashi

  2. Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, 11800, Penang, Malaysia

    Mehrbakhsh Nilashi

  3. Department of Computer Science and Engineering, Qatar University, 2713, Doha, Qatar

    Rabab Ali Abumalloh

  4. Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK

    Hossein Ahmadi

  5. University of Plymouth, Centre for Health Technology, Faculty of Health, Plymouth, PL4 8AA, UK

    Hossein Ahmadi

  6. Department of Business Administration, College of Business Administration, Princess Nourah Bint Abdulrahman University, 11671, Riyadh, Saudi Arabia

    Sarminah Samad

  7. Computer Science Department, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia

    Sultan Alyami

  8. Information Systems Department, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia

    Abdullah Alghamdi & Mesfer Alrizq

  9. Scientific and Engineering Research Center (SERC), Najran University, Najran, Saudi Arabia

    Sultan Alyami, Abdullah Alghamdi & Mesfer Alrizq

  10. Primary Care Medicine Department, Faculty of Medicine, Universiti Teknologi MARA, 47000, Sungai Buloh, Selangor, Malaysia

    Salma Yasmin Mohd Yusuf

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  1. Mehrbakhsh Nilashi
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  8. Salma Yasmin Mohd Yusuf
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Correspondence to Mehrbakhsh Nilashi.

Appendix A

Appendix A

See Tables 5 and 6.

Table 5 Clustering results for the PD features
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Table 6 Clustering results for the outputs of the dataset
Full size table

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Nilashi, M., Abumalloh, R.A., Ahmadi, H. et al. Accuracy Analysis of Type-2 Fuzzy System in Predicting Parkinson’s Disease Using Biomedical Voice Measures. Int. J. Fuzzy Syst. 26, 1261–1284 (2024). https://doi.org/10.1007/s40815-023-01665-0

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