Application and Analysis of Random Forest and Support Vector Classification in Risk Prediction of Childhood Obesity and Hyperuricemia
RF and SVC models offer reliable tools for early risk prediction of obesity and hyperuricemia in children, each tailored to distinct clinical scenarios. These findings support early identification and targeted intervention. Future studies will explore the integration of metabolomic data and ensemble approaches to further enhance model performance and clinical applicability.
Authors
- Jin Cai (ORCID: https://orcid.org/0000-0003-1597-8143)
- Shi Yun-long (ORCID: https://orcid.org/0000-0002-8338-0154)
- Xinghua Wei (ORCID: https://orcid.org/0000-0002-0883-3694)
- Yan Jing Wu (ORCID: https://orcid.org/0000-0002-4162-3322)
- Yuhang Wang (ORCID: https://orcid.org/0009-0002-0167-419X)
- Shuang Shi (ORCID: https://orcid.org/0000-0002-8134-0300)
Institutions
- Nantong University (CN)
- Affiliated Hospital of Nantong University (CN)
Publication Details
- Journal
- Diabetes Metabolic Syndrome and Obesity
- Published
- 2025-07-01
- DOI
- https://doi.org/10.2147/dmso.s519284
- Citations
- 1
- Primary Topic
- Traditional Chinese Medicine Studies
- Type
- article
- Field-Weighted Citation Impact
- 2.38