Machine learning-based risk prediction model for cognitive dysfunction in elderly individuals
Age, race, education level, diabetes, and depression are significant influencing factors of cognitive dysfunction in the elderly. Among the ML algorithms evaluated, the Random Forest model exhibited the best predictive performance.
Authors
- Ruikai Wu (ORCID: https://orcid.org/0000-0002-8738-9623)
- Wen‐Jone Chen (ORCID: https://orcid.org/0000-0002-2098-5922)
- Ting Zou (ORCID: https://orcid.org/0000-0002-6442-4895)
- Lei Zhang (ORCID: https://orcid.org/0009-0007-8798-336X)
- Xuan Xiang
- Haijun Miao (ORCID: https://orcid.org/0009-0009-0477-0567)
- Xiaohui Zhou (ORCID: https://orcid.org/0000-0001-7491-628X)
Institutions
- Xinjiang Medical University (CN)
- First Affiliated Hospital of Xinjiang Medical University (CN)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-12-19
- DOI
- https://doi.org/10.1371/journal.pone.0336058
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00