Machine learning ensemble meets clinical practice: developing a real-world risk prediction model for metabolic syndrome using super learner and scorecard approaches
The super learner model provides a highly accurate tool for MetS risk prediction, whereas the risk scorecard offers a practical and interpretable solution for clinical and personal use. These models enable precise risk assessment to guide prevention and improve outcomes.
Authors
- Shuwen Li (ORCID: https://orcid.org/0000-0002-7867-908X)
- Kailu Fang (ORCID: https://orcid.org/0000-0003-2808-2071)
- Yushi Lin (ORCID: https://orcid.org/0000-0002-9206-0419)
- Kang Fu (ORCID: https://orcid.org/0000-0002-2725-396X)
- Luyan Zheng
- Jie Wu (ORCID: https://orcid.org/0000-0002-6995-1213)
- Yu Zhang (ORCID: https://orcid.org/0000-0001-8296-154X)
Institutions
- Shandong Provincial Hospital (CN)
- National Centre for Infectious Diseases (SG)
- Second Affiliated Hospital of Zhejiang University (CN)
- Shandong First Medical University (CN)
- First Affiliated Hospital Zhejiang University (CN)
Publication Details
- Journal
- Journal of Advanced Research
- Published
- 2025-06-27
- DOI
- https://doi.org/10.1016/j.jare.2025.06.072
- Citations
- 2
- Primary Topic
- Diabetes, Cardiovascular Risks, and Lipoproteins
- Type
- article
- Field-Weighted Citation Impact
- 2.59