Explainable AI for Parkinson’s disease prediction: A machine learning approach with interpretable models
The study demonstrated the effectiveness of an interpretable RF model for accurate PD prediction. Integration of ML and XAI significantly improves clinical decision-making, diagnosis timing, and personalized patient care.
Authors
- Ibrahim Adeyanju (ORCID: https://orcid.org/0000-0002-6163-2453)
- Adebimpe Esan (ORCID: https://orcid.org/0000-0002-4059-9519)
- Bolaji A Omodunbi
- David B. Olawade (ORCID: https://orcid.org/0000-0003-0188-9836)
- Nicholas Aderinto (ORCID: https://orcid.org/0000-0003-0004-7389)
- Afeez Adekunle Soladoye (ORCID: https://orcid.org/0000-0002-6349-5173)
Institutions
- University of East London (GB)
- Federal University Oye Ekiti (NG)
- Ladoke Akintola University of Technology (NG)
Publication Details
- Journal
- Current Research in Translational Medicine
- Published
- 2025-09-05
- DOI
- https://doi.org/10.1016/j.retram.2025.103541
- Citations
- 6
- Primary Topic
- Parkinson's Disease Mechanisms and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 8.34