Development and internal validation of a machine learning algorithm for the risk of type 2 diabetes mellitus in children with obesity
The rising prevalence of obesity in childhood is associated with an increase in the risk of early-onset T2DM. Therefore, the early identification of individuals at high risk is crucial to prevent the development of this disease. In a comparative analysis of the performance of multiple ML algorithms, we found that the SVM algorithm was the best predictor of the development of T2DM.
Authors
- Yue Liu (ORCID: https://orcid.org/0009-0006-9442-3267)
- Zisheng Ai (ORCID: https://orcid.org/0000-0002-6264-0965)
- Yayun Wang (ORCID: https://orcid.org/0000-0002-0397-0390)
- Rong Huang (ORCID: https://orcid.org/0000-0001-6256-7481)
- Jianmin Zhang (ORCID: https://orcid.org/0000-0003-3271-246X)
- Guoying Wang (ORCID: https://orcid.org/0000-0002-0304-0986)
- Siyuan Cao (ORCID: https://orcid.org/0000-0003-2444-9657)
- Jin‐Xia Yang (ORCID: https://orcid.org/0000-0002-3796-1513)
- Huimin Zhou (ORCID: https://orcid.org/0000-0001-9433-0518)
- Haiying Wu (ORCID: https://orcid.org/0009-0005-0264-1703)
Institutions
- Tongji University (CN)
- Second Military Medical University (CN)
- Soochow University (CN)
- Suzhou Traditional Chinese Medicine Hospital (CN)
- Children's Hospital of Suzhou University (CN)
- Shanghai Pudong New Area Gongli Hospital (CN)
- Shanghai First Maternity and Infant Hospital (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-08-08
- DOI
- https://doi.org/10.3389/fendo.2025.1649988
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.94