Predicting superagers: a machine learning approach utilizing gut microbiome features
Our findings demonstrate the machine learning-based predictive models using gut-microbiome features can differentiate superagers from typical agers with a reasonable performance.
Authors
- Jee Hyang Jeong (ORCID: https://orcid.org/0000-0001-7945-6956)
- Bori R. Kim (ORCID: https://orcid.org/0000-0002-8069-1213)
- Geon Ha Kim (ORCID: https://orcid.org/0000-0001-5395-1924)
- Ha Eun Kim (ORCID: https://orcid.org/0000-0002-7297-2832)
- Sang Hi Hong
- Seung Yeon Song
Institutions
- Ewha Womans University (KR)
- Ewha Womans University Medical Center (KR)
- Artificial Intelligence in Medicine (Canada) (CA)
- Korea Research Institute of Bioscience and Biotechnology (KR)
Publication Details
- Journal
- Frontiers in Aging Neuroscience
- Published
- 2024-09-09
- DOI
- https://doi.org/10.3389/fnagi.2024.1444998
- Citations
- 6
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.08
Funders
- National Research Foundation
- Ewha Womans University
- Korea Health Industry Development Institute
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea
- Institute for Information and Communications Technology Promotion