Parkinson's Disease Diagnosis Using Laplacian Score, Gaussian Process Regression and Self-Organizing Maps

Mehrbakhsh Nilashi; Rabab Ali Abumalloh; Sultan Alyami; Abdullah Alghamdi; Mesfer Alrizq

doi:10.3390/brainsci13040543

Parkinson's Disease Diagnosis Using Laplacian Score, Gaussian Process Regression and Self-Organizing Maps

Brain Sci. 2023 Mar 24;13(4):543. doi: 10.3390/brainsci13040543.

Authors

Mehrbakhsh Nilashi^{1

2}, Rabab Ali Abumalloh³, Sultan Alyami⁴, Abdullah Alghamdi⁵, Mesfer Alrizq⁵

Affiliations

¹ UCSI Graduate Business School, UCSI University, No. 1 Jalan Menara Gading, UCSI Heights, Cheras, Kuala Lumpur 56000, Malaysia.
² Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia-USM, Penang 11800, Malaysia.
³ Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar.
⁴ Computer Science Department, College of Computer Science and Information Systems, Najran University, Najran 55461, Saudi Arabia.
⁵ Information Systems Department, College of Computer Science and Information Systems, Najran University, Najran 55461, Saudi Arabia.

Abstract

Parkinson's disease (PD) is a complex degenerative brain disease that affects nerve cells in the brain responsible for body movement. Machine learning is widely used to track the progression of PD in its early stages by predicting unified Parkinson's disease rating scale (UPDRS) scores. In this paper, we aim to develop a new method for PD diagnosis with the aid of supervised and unsupervised learning techniques. Our method is developed using the Laplacian score, Gaussian process regression (GPR) and self-organizing maps (SOM). SOM is used to segment the data to handle large PD datasets. The models are then constructed using GPR for the prediction of the UPDRS scores. To select the important features in the PD dataset, we use the Laplacian score in the method. We evaluate the developed approach on a PD dataset including a set of speech signals. The method was evaluated through root-mean-square error (RMSE) and adjusted R-squared (adjusted R²). Our findings reveal that the proposed method is efficient in the prediction of UPDRS scores through a set of speech signals (dysphonia measures). The method evaluation showed that SOM combined with the Laplacian score and Gaussian process regression with the exponential kernel provides the best results for R-squared (Motor-UPDRS = 0.9489; Total-UPDRS = 0.9516) and RMSE (Motor-UPDRS = 0.5144; Total-UPDRS = 0.5105) in predicting UPDRS compared with the other kernels in Gaussian process regression.

Keywords: Gaussian process regression; Laplacian score; Parkinson’s disease; UPDRS prediction; self-organizing maps.

Grants and funding

The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work, under the Research Groups Funding program grant code NU/RG/SERC/12/12.