Application of machine learning algorithms in predicting new onset hypertension: a study based on the China Health and Nutrition Survey
We used the AMFormer model for the first time in predicting new onset hypertension and achieved the best results among the six algorithms tested. Key features associated with new onset hypertension can be determined through this algorithm. The practice of machine learning algorithms can further enhance the predictive efficacy of diseases and identify risk factors for diseases.
Authors
- Manhui Zhang
- Xianfeng Xia (ORCID: https://orcid.org/0000-0003-4718-8189)
- Yue Pan (ORCID: https://orcid.org/0000-0002-0521-250X)
- Zhigang Wang (ORCID: https://orcid.org/0000-0001-6524-4098)
- Qiqi Wang (ORCID: https://orcid.org/0000-0003-2443-6370)
- G. Y. Zhang (ORCID: https://orcid.org/0009-0006-9571-6037)
Institutions
- Capital Medical University (CN)
- Chinese PLA General Hospital (CN)
- Beijing Tian Tan Hospital (CN)
- National Clinical Research Center for Digestive Diseases (CN)
- Guidance (United Kingdom) (GB)
- Taiwan Centers for Disease Control (TW)
Publication Details
- Journal
- Environmental Health and Preventive Medicine
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1265/ehpm.24-00270
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.94
Funders
- National Institute for Nutrition and Health
- National Institutes of Health
- China-Japan Friendship Hospital
- National Institute on Aging
- National Heart, Lung, and Blood Institute
- Fogarty International Center
- National Institute of Diabetes and Digestive and Kidney Diseases
- National Institute of Child Health and Human Development
- Eunice Kennedy Shriver National Institute of Child Health and Human Development
- Carolina Population Center, University of North Carolina at Chapel Hill