Risk factors associated with severe progression of Parkinson’s disease: random forest and logistic regression models
The RF model provides better predictive accuracy and discriminatory power compared to Logistic Regression in identifying risk factors for severe PD progression. This study highlights the potential of machine learning techniques like Random Forest for early risk stratification and personalized management of PD.
Authors
- Elbert S. Huang (ORCID: https://orcid.org/0000-0002-4628-2061)
- Qian Zhang (ORCID: https://orcid.org/0000-0003-1570-5380)
- Hao Yang (ORCID: https://orcid.org/0000-0002-1772-078X)
- Hongping Wan (ORCID: https://orcid.org/0009-0001-6266-7674)
- Jie Tan
Institutions
- Jianghan University (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-04-07
- DOI
- https://doi.org/10.3389/fneur.2025.1550789
- Citations
- 3
- Primary Topic
- Parkinson's Disease Mechanisms and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 4.17