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

Waleed Abdu Zogaan; Mehrbakhsh Nilashi; Hossein Ahmadi; Rabab Ali Abumalloh; Mesfer Alrizq; Hamad Abosaq; Abdullah Alghamdi

doi:10.1016/j.mex.2024.102553

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

MethodsX. 2024 Jan 5:12:102553. doi: 10.1016/j.mex.2024.102553. eCollection 2024 Jun.

Authors

Waleed Abdu Zogaan¹, Mehrbakhsh Nilashi^{2

3}, Hossein Ahmadi^{4

5}, Rabab Ali Abumalloh⁶, Mesfer Alrizq^{7

8}, Hamad Abosaq⁹, Abdullah Alghamdi^{7

8}

Affiliations

¹ Department of Computer Science, Faculty of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi Arabia.
² UCSI Graduate Business School, UCSI University, Cheras, Kuala Lumpur 56000, Malaysia.
³ Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, Penang, 11800, Malaysia.
⁴ Centre for Health Technology, Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK.
⁵ Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK.
⁶ Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar.
⁷ Information Systems Department, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia.
⁸ Scientific and Engineering Research Center (SERC), Najran University, Najran, Saudi Arabia.
⁹ Computer Science Department, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia.

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.

Keywords: A Combined Method of Optimized Learning Vector Quantization and Neuro-Fuzzy Techniques; Learning vector quantization; Motor-UPDRS; Neuro-fuzzy; Optimized learning rate; Parkinson's disease; Total-UPDRS.