Development and validation of deep learning- and ensemble learning-based biological ages in the NHANES study
Our study successfully developed and validated two ML-based BA models capable of accurately predicting both all-cause and cause-specific mortality. These findings suggest that the DBA and EnBA models hold promise for early identification of high-risk individuals, potentially facilitating timely preventive interventions and improving population health outcomes.
Authors
- Xueling Yang (ORCID: https://orcid.org/0000-0001-8403-6833)
- Yue Dong (ORCID: https://orcid.org/0000-0003-2161-8566)
- Wenyuan Li (ORCID: https://orcid.org/0000-0002-8853-225X)
- Yushu Huang (ORCID: https://orcid.org/0000-0002-0504-0234)
- Qi Wang (ORCID: https://orcid.org/0009-0004-4055-5278)
- Adila Abula
Institutions
- Second Affiliated Hospital of Zhejiang University (CN)
Publication Details
- Journal
- Frontiers in Aging Neuroscience
- Published
- 2025-07-16
- DOI
- https://doi.org/10.3389/fnagi.2025.1532884
- Citations
- 1
- Primary Topic
- Health, Environment, Cognitive Aging
- Type
- article
- Field-Weighted Citation Impact
- 1.20
Funders
- National Natural Science Foundation of China