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

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 appro...

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Published in:INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
Main Authors: Nilashi, Mehrbakhsh; Abumalloh, Rabab Ali; Ahmadi, Hossein; Samad, Sarminah; Alyami, Sultan; Alghamdi, Abdullah; Alrizq, Mesfer; Yusuf, Salma Yasmin Mohd
Format: Article; Early Access
Language:English
Published: SPRINGER HEIDELBERG 2024
Subjects:
Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001169638000001
author Nilashi
Mehrbakhsh; Abumalloh
Rabab Ali; Ahmadi
Hossein; Samad
Sarminah; Alyami
Sultan; Alghamdi
Abdullah; Alrizq
Mesfer; Yusuf
Salma Yasmin Mohd
spellingShingle Nilashi
Mehrbakhsh; Abumalloh
Rabab Ali; Ahmadi
Hossein; Samad
Sarminah; Alyami
Sultan; Alghamdi
Abdullah; Alrizq
Mesfer; Yusuf
Salma Yasmin Mohd
Accuracy Analysis of Type-2 Fuzzy System in Predicting Parkinson's Disease Using Biomedical Voice Measures
Automation & Control Systems; Computer Science
author_facet Nilashi
Mehrbakhsh; Abumalloh
Rabab Ali; Ahmadi
Hossein; Samad
Sarminah; Alyami
Sultan; Alghamdi
Abdullah; Alrizq
Mesfer; Yusuf
Salma Yasmin Mohd
author_sort Nilashi
spelling Nilashi, Mehrbakhsh; Abumalloh, Rabab Ali; Ahmadi, Hossein; Samad, Sarminah; Alyami, Sultan; Alghamdi, Abdullah; Alrizq, Mesfer; Yusuf, Salma Yasmin Mohd
Accuracy Analysis of Type-2 Fuzzy System in Predicting Parkinson's Disease Using Biomedical Voice Measures
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
English
Article; Early Access
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.
SPRINGER HEIDELBERG
1562-2479
2199-3211
2024


10.1007/s40815-023-01665-0
Automation & Control Systems; Computer Science

WOS:001169638000001
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001169638000001
title Accuracy Analysis of Type-2 Fuzzy System in Predicting Parkinson's Disease Using Biomedical Voice Measures
title_short Accuracy Analysis of Type-2 Fuzzy System in Predicting Parkinson's Disease Using Biomedical Voice Measures
title_full Accuracy Analysis of Type-2 Fuzzy System in Predicting Parkinson's Disease Using Biomedical Voice Measures
title_fullStr Accuracy Analysis of Type-2 Fuzzy System in Predicting Parkinson's Disease Using Biomedical Voice Measures
title_full_unstemmed Accuracy Analysis of Type-2 Fuzzy System in Predicting Parkinson's Disease Using Biomedical Voice Measures
title_sort Accuracy Analysis of Type-2 Fuzzy System in Predicting Parkinson's Disease Using Biomedical Voice Measures
container_title INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
language English
format Article; Early Access
description 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.
publisher SPRINGER HEIDELBERG
issn 1562-2479
2199-3211
publishDate 2024
container_volume
container_issue
doi_str_mv 10.1007/s40815-023-01665-0
topic Automation & Control Systems; Computer Science
topic_facet Automation & Control Systems; Computer Science
accesstype
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url https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001169638000001
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