A combined method of optimized learning vector quantization and neuro-fuzzy techniques for predicting unified Parkinson&apos;s disease rating scale using vocal features

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Abstract

Parkinson's Disease (PD) is a common disorder of the central nervous system. The Unified Parkinson's Disease Rating Scale or UPDRS is commonly used to track PD symptom progression because it displays the presence and severity of symptoms. To model the relationship between speech signal properties and UPDRS scores, this study develops a new method using Neuro-Fuzzy (ANFIS) and Optimized Learning Rate Learning Vector Quantization (OLVQ1). ANFIS is developed for different Membership Functions (MFs). The method is evaluated using Parkinson's telemonitoring dataset which includes a total of 5875 voice recordings from 42 individuals in the early stages of PD which comprises 28 men and 14 women. The dataset is comprised of 16 vocal features and Motor-UPDRS, and Total-UPDRS. The method is compared with other learning techniques. The results show that OLVQ1 combined with the ANFIS has provided the best results in predicting Motor-UPDRS and Total-UPDRS. The lowest Root Mean Square Error (RMSE) values (UPDRS (Total)=0.5732; UPDRS (Motor)=0.5645) and highest R-squared values (UPDRS (Total)=0.9876; UPDRS (Motor)=0.9911) are obtained by this method. The results are discussed and directions for future studies are presented.

i.

ANFIS and OLVQ1 are combined to predict UPDRS.
ii.

OLVQ1 is used for PD data segmentation.
iii.

ANFIS is developed for different MFs to predict Motor-UPDRS and Total-UPDRS.

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Most cited references 23

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Can a Smartphone Diagnose Parkinson Disease? A Deep Neural Network Method and Telediagnosis System Implementation

Y. N. Zhang (2017)

Parkinson's disease (PD) is primarily diagnosed by clinical examinations, such as walking test, handwriting test, and MRI diagnostic. In this paper, we propose a machine learning based PD telediagnosis method for smartphone. Classification of PD using speech records is a challenging task owing to the fact that the classification accuracy is still lower than doctor-level. Here we demonstrate automatic classification of PD using time frequency features, stacked autoencoders (SAE), and K nearest neighbor (KNN) classifier. KNN classifier can produce promising classification results from useful representations which were learned by SAE. Empirical results show that the proposed method achieves better performance with all tested cases across classification tasks, demonstrating machine learning capable of classifying PD with a level of competence comparable to doctor. It concludes that a smartphone can therefore potentially provide low-cost PD diagnostic care. This paper also gives an implementation on browser/server system and reports the running time cost. Both advantages and disadvantages of the proposed telediagnosis system are discussed.

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Author and article information

Contributors

Mehrbakhsh Nilashi

Journal

Journal ID (nlm-ta): MethodsX

Journal ID (iso-abbrev): MethodsX

Title: MethodsX

Publisher: Elsevier

ISSN (Electronic): 2215-0161

Publication date PMC-release: 05 January 2024

Publication date Collection: June 2024

Publication date (Electronic): 05 January 2024

Volume: 12

Electronic Location Identifier: 102553

Affiliations

[a ]Department of Computer Science, Faculty of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi Arabia

[b ]UCSI Graduate Business School, UCSI University, Cheras, Kuala Lumpur 56000, Malaysia

[c ]Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, Penang, 11800, Malaysia

[d ]Centre for Health Technology, Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK

[e ]Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK

[f ]Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar

[g ]Information Systems Department, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia

[h ]Scientific and Engineering Research Center (SERC), Najran University, Najran, Saudi Arabia

[i ]Computer Science Department, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia

Author notes

[* ]Corresponding author. mehrbakhsh@ 123456ucsiuniversity.edu.my

Article

Publisher Item ID: S2215-0161(24)00008-6 Publisher ID: 102553

DOI: 10.1016/j.mex.2024.102553

PMC ID: 10825686

PubMed ID: 38292319

SO-VID: 29dbc5b0-c43b-4ae2-91eb-331c18f14d77

License:

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

History

Date received : 7 November 2023

Date accepted : 4 January 2024

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