Development of Fall Risk Classification Models for Community-Dwelling Older Adults using Latent Class Analysis and Machine Learning
The study adopted a preventive approach by differentiating among low-, moderate-, and high-fall-risk groups, thus providing valuable insights for healthcare professionals. Identifying these risk factors can support the development of customized fall prevention programs for older adults.
Authors
- Suyeong Bae (ORCID: https://orcid.org/0000-0003-0739-9424)
- Ickpyo Hong (ORCID: https://orcid.org/0000-0001-5404-7646)
- Jong‐Bae Kim (ORCID: https://orcid.org/0000-0002-6502-2281)
- Daewoo Pak (ORCID: https://orcid.org/0000-0001-8286-8409)
- Mi Jung Lee (ORCID: https://orcid.org/0000-0002-9677-5788)
- Eun-Young Yoo
Institutions
- Yonsei University (KR)
Publication Details
- Journal
- Gerontology
- Published
- 2025-02-20
- DOI
- https://doi.org/10.1159/000544779
- Citations
- 1
- Primary Topic
- Education and Learning Interventions
- Type
- article
- Field-Weighted Citation Impact
- 3.25