Factors associated with underweight, overweight, and obesity in Chinese children aged 3–14 years using ensemble learning algorithms
Our findings indicate that random forest is the best ensemble learning algorithm for predicting underweight, overweight, and obesity in children aged 3-14 years. We identified the optimal set of significant factors for each malnutrition status and incorporated them into a web application to support the application of this study's findings.
Authors
- Kening Chen
- Wenquan Niu (ORCID: https://orcid.org/0000-0003-1715-3372)
- Xiaoqian Zhang (ORCID: https://orcid.org/0000-0002-0311-8958)
- Qiong Wang (ORCID: https://orcid.org/0000-0001-5673-5063)
- Zhixin Zhang (ORCID: https://orcid.org/0000-0002-4914-1969)
- Fangjieyi Zheng
Institutions
- Beijing University of Chinese Medicine (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- China-Japan Friendship Hospital (CN)
- Capital Institute of Pediatrics (CN)
Publication Details
- Journal
- Journal of Global Health
- Published
- 2025-02-06
- DOI
- https://doi.org/10.7189/jogh.15.04013
- Citations
- 4
- Primary Topic
- Child Nutrition and Water Access
- Type
- article
- Field-Weighted Citation Impact
- 4.48
Funders
- National Natural Science Foundation of China
- Beijing University of Chinese Medicine
- China-Japan Friendship Hospital