AI-Driven Fall Prediction across Generations: Integrating Deep Learning and Machine Learning for Young, Middle-Aged, and Older Adults
We developed a robust and interpretable DL model to identify fall risk across different age-groups. Age-specific risk factors highlight the need for tailored preventive strategies. External validation using an independent dataset demonstrated moderate generalizability. Validating the model on larger, more diverse datasets and integration of sequential or sensor-based data are essential for practical applications.
Authors
- Chen‐Cheng Yang (ORCID: https://orcid.org/0000-0002-6361-3854)
- Chih‐Hsing Hung (ORCID: https://orcid.org/0000-0002-2026-5108)
- Fa-Chen Lin (ORCID: https://orcid.org/0009-0002-3587-6160)
- Po‐Hung Chen (ORCID: https://orcid.org/0000-0002-8018-131X)
- Cheng-Hong Yang
- Hung‐Yi Chuang (ORCID: https://orcid.org/0000-0002-8321-8720)
Institutions
- Tainan University of Technology (TW)
- Kaohsiung Medical University (TW)
- Kaohsiung Medical University Chung-Ho Memorial Hospital (TW)
- Chia-Yi Christian Hospital (TW)
- Kaohsiung Municipal Hsiao-Kang Hospital (TW)
- National Kaohsiung University of Science and Technology (TW)
Publication Details
- Journal
- Gerontology
- Published
- 2025-11-06
- DOI
- https://doi.org/10.1159/000549410
- Citations
- 1
- Primary Topic
- Balance, Gait, and Falls Prevention
- Type
- article
- Field-Weighted Citation Impact
- 4.76