Using machine learning to identify risk factors for Alzheimer's disease among older adults in the United States: The role of chronic and behavioral health
Machine learning effectively delineates complex AD risk profiles, highlighting the roles of vascular and metabolic comorbidities and the modifying effects of sex, osteoporosis, and COVID-19. These insights support targeted screening and early intervention strategies to improve outcomes in older adults.
Authors
- Md Roungu Ahmmad (ORCID: https://orcid.org/0000-0002-3886-5777)
- Md Tareq Ferdous Khan (ORCID: https://orcid.org/0000-0002-2460-0604)
- Md. Emran Hossain (ORCID: https://orcid.org/0000-0001-6882-0177)
- Sumitra Paudel (ORCID: https://orcid.org/0000-0003-2017-3278)
Institutions
- University of Central Florida (US)
- University of South Florida (US)
- University of Southern Mississippi (US)
- University of South Florida St. Petersburg (US)
- Clemson University (US)
Publication Details
- Journal
- Journal of Alzheimer s Disease Reports
- Published
- 2025-09-10
- DOI
- https://doi.org/10.1177/25424823251377691
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00