Enhancing Parkinson’s Disease Diagnosis Through Stacking Ensemble-Based Machine Learning Approach-Reference-Cited by-同舟云学术

Enhancing Parkinson’s Disease Diagnosis Through Stacking Ensemble-Based Machine Learning Approach

Published:2024 Issue: Volume:12 Page:79549-79567
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Al-Tam Riyadh M.¹^ORCID,Hashim Fatma A.²^ORCID,Maqsood Sarmad³^ORCID,Abualigah Laith⁴^ORCID,Alwhaibi Reem M.⁵^ORCID

Affiliation:

1. School of Computational Sciences, Swami Ramanand Teerth Marathwada University, Nanded, Maharashtra, India

2. Faculty of Engineering, Helwan University, Cairo, Egypt

3. Centre of Real Time Computer Systems, Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania

4. Computer Science Department, Al Al-Bayt University, Mafraq, Jordan

5. Department of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah Bint Abdulrahman University, P. O. Box 84428, Riyadh, Saudi Arabia

Funder

Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia, through the Researchers’ Supporting Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10546910.pdf?arnumber=10546910

Reference50 articles.

1. No genetic evidence for involvement of alcohol dehydrogenase genes in risk for Parkinson's disease

2. Handwritten pattern recognition for early Parkinson’s disease diagnosis

3. Hybrid optimization enabled deep learning model for Parkinson's disease classification

4. Parkinson’s Disease Detection Using Hybrid LSTM-GRU Deep Learning Model

5. Non-motor symptoms of Parkinson's disease: dopaminergic pathophysiology and treatment

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