Affiliations 

  • 1 Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, USM, Penang 11800, Malaysia
  • 2 Computer Department, Applied College, Imam Abdulrahman Bin Faisal University, Dammam 1982, Saudi Arabia
  • 3 School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
  • 4 Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
  • 5 Department of MIS, Dhofar University, Salalah, Oman
  • 6 Computer Science Department, College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia
  • 7 Department of Computer Science, Faculty of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi Arabia
J Healthc Eng, 2022;2022:2793361.
PMID: 35154618 DOI: 10.1155/2022/2793361

Abstract

Parkinson's disease (PD) is a complex neurodegenerative disease. Accurate diagnosis of this disease in the early stages is crucial for its initial treatment. This paper aims to present a comparative study on the methods developed by machine learning techniques in PD diagnosis. We rely on clustering and prediction learning approaches to perform the comparative study. Specifically, we use different clustering techniques for PD data clustering and support vector regression ensembles to predict Motor-UPDRS and Total-UPDRS. The results are then compared with the other prediction learning approaches, multiple linear regression, neurofuzzy, and support vector regression techniques. The comparative study is performed on a real-world PD dataset. The prediction results of data analysis on a PD real-world dataset revealed that expectation-maximization with the aid of SVR ensembles can provide better prediction accuracy in relation to decision trees, deep belief network, neurofuzzy, and support vector regression combined with other clustering techniques in the prediction of Motor-UPDRS and Total-UPDRS.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.