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

Authors

Hossein Ahmadi, UoP RU - Centre for Health Technology
Mehrbakhsh Nilashi, Universiti Sains Malaysia
Mesfer Alrziq, Najran University
Abdullah Alghamdi, Najran University
Waleed Abdu Zogaan

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.

DOI Link

10.1016/j.mex.2024.102553

Publication Date

2024-01-01

Publication Title

MethodsX

Volume

12

Acceptance Date

2024-01-04

Deposit Date

2024-07-24

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.

Additional Links

https://www.scopus.com/pages/publications/85182365825

Keywords

Learning vector quantization, Motor-UPDRS, Neuro-fuzzy, Optimized learning rate, Parkinson's disease, Total-UPDRS

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Ahmadi, H., Nilashi, M., Alrziq, M., Alghamdi, A., & Zogaan, W. (2024) 'A combined method of optimized learning vector quantization and neuro-fuzzy techniques for predicting unified Parkinson's disease rating scale using vocal features', MethodsX, 12. Available at: 10.1016/j.mex.2024.102553