Predictive factors for dementia among older adults in South Korea: an interpretable machine learning analysis
Age and education were the strongest predictors of cognitive function, while modifiable factors-including oral health, depression, social engagement, and IADL performance-played significant roles across subgroups. This interpretable machine learning approach revealed nuanced patterns of predictor importance across cognitive status, age, and sex, underscoring the value of targeted interventions to reduce dementia risk in aging populations.
Authors
- Junhyup Lee (ORCID: https://orcid.org/0000-0003-4589-9673)
- Minsung Sohn (ORCID: https://orcid.org/0000-0001-7748-5622)
- Jungyeon Yang (ORCID: https://orcid.org/0000-0001-8103-1594)
- Daeyoung Choi (ORCID: https://orcid.org/0000-0001-6323-4478)
Institutions
- Korea University (KR)
- Korea Soongsil Cyber University (KR)
- Cyber University of Korea (KR)
- Korea University (JP)
Publication Details
- Journal
- Alzheimer s Research & Therapy
- Published
- 2025-11-13
- DOI
- https://doi.org/10.1186/s13195-025-01871-y
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Korea University