Affiliations 

  • 1 UCSI Graduate Business School, UCSI University, No. 1 Jalan Menara Gading, UCSI Heights, 56000 Cheras, Kuala Lumpur, Malaysia; Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, 11800, USM Penang, Malaysia. Electronic address: nilashidotnet@hotmail.com
  • 2 Computer Department, Applied College, Imam Abdulrahman Bin Faisal University, P.O. Box. 1982, Dammam, Saudi Arabia
  • 3 Primary Care Medicine Department, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh 47000, Selangor, Malaysia
  • 4 Institute of Research and Development, Duy Tan University, Da Nang, VietNam; International School, Duy Tan University, Da Nang, VietNam. Electronic address: hntha@duytan.edu.vn
  • 5 Computer Science Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
Comput Biol Chem, 2023 Feb;102:107788.
PMID: 36410240 DOI: 10.1016/j.compbiolchem.2022.107788

Abstract

Predicting Unified Parkinson's Disease Rating Scale (UPDRS) in Total- UPDRS and Motor-UPDRS clinical scales is an important part of controlling PD. Computational intelligence approaches have been used effectively in the early diagnosis of PD by predicting UPDRS. In this research, we target to present a combined approach for PD diagnosis using an ensemble learning approach with the ability of online learning from clinical large datasets. The method is developed using Deep Belief Network (DBN) and Neuro-Fuzzy approaches. A clustering approach, Expectation-Maximization (EM), is used to handle large datasets. The Principle Component Analysis (PCA) technique is employed for noise removal from the data. The UPDRS prediction models are constructed for PD diagnosis. To handle the missing data, K-NN is used in the proposed method. We use incremental machine learning approaches to improve the efficiency of the proposed method. We assess our approach on a real-world PD dataset and the findings are assessed compared to other PD diagnosis approaches developed by machine learning techniques. The findings revealed that the approach can improve the UPDRS prediction accuracy and the time complexity of previous methods in handling large datasets.

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