A Comprehensive Framework for Parkinson's Disease Detection Using Spiral Drawings and Advanced Machine Learning Techniques
The study demonstrated that combining advanced techniques for feature extraction, selection, and classification can significantly improve PD detection accuracy. Future research should focus on integrating multiple data sources and exploring real-time applications to enhance scalability and clinical utility.
Authors
- Bagher Farhood (ORCID: https://orcid.org/0000-0003-2290-7220)
- Anupam Yadav (ORCID: https://orcid.org/0000-0002-9179-3151)
- Waleed K. Abdulsahib (ORCID: https://orcid.org/0000-0002-8851-5783)
- J Gowrishankar
- Kamal Kishore Joshi (ORCID: https://orcid.org/0000-0001-5067-6867)
- Fadhil Feez Sead
- Hardik Doshi
- Mohamed J. Saadh (ORCID: https://orcid.org/0009-0004-9146-2477)
- Nargiza Mansurova
- Mayank Kundlas
Institutions
- Jain University (IN)
- Kashan University of Medical Sciences (IR)
- Iraqi University (IQ)
- Marwadi University (IN)
- Interstate Commission for Water Coordination of Central Asia (UZ)
- University of Al-Qadisiyah (IQ)
- Tashkent Pediatric Medical Institute (UZ)
- Graphic Era University (IN)
- Chitkara University (IN)
- GLA University (IN)
- Middle East University (JO)
Publication Details
- Journal
- Brain and Behavior
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1002/brb3.70770
- Citations
- 1
- Primary Topic
- Parkinson's Disease Mechanisms and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.39