Document Type
Article
Publication Date
7-1-2025
Department
Department of Computer Science
Abstract
Parkinson’s disease (PD) is a devastating neurological ailment affecting both mobility and cognitive function, posing considerable problems to the health of the elderly across the world. The absence of a conclusive treatment underscores the requirement to investigate cutting-edge diagnostic techniques to improve patient outcomes. Machine learning (ML) has the potential to revolutionize PD detection by applying large repositories of structured data to enhance diagnostic accuracy. 133 papers published between 2021 and April 2024 were reviewed using a systematic literature review (SLR) methodology, and subsequently classified into five categories: acoustic data, biomarkers, medical imaging, movement data, and multimodal datasets. This comprehensive analysis offers valuable insights into the applications of ML in PD diagnosis. Our SLR identifies the datasets and ML algorithms used for PD diagnosis, as well as their merits, limitations, and evaluation factors. We also discuss challenges, future directions, and outstanding issues.
Publication Title
Npj Parkinson S Disease
Recommended Citation
Shokrpour, S.,
MoghadamFarid, A.,
Bazzaz Abkenar, S.,
Haghi Kashani, M.,
Akbari, M.,
&
Sarvizadeh, M.
(2025).
Machine learning for Parkinson’s disease: a comprehensive review of datasets, algorithms, and challenges.
Npj Parkinson S Disease,
11(1).
http://doi.org/10.1038/s41531-025-01025-9
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/1839
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© The Author(s) 2025. Publisher’s version of record: https://doi.org/10.1038/s41531-025-01025-9