Machine learning prediction and SHAP interpretability analysis of heart failure risk in patients with hyperuricemia
Integration of these six readily available indicators provides a simple, interpretable tool for HF risk stratification in hyperuricemia patients. While further longitudinal and multicenter validation is needed, the model shows promise for early identification and targeted intervention in clinical practice.
Authors
- LI Shu-hu (ORCID: https://orcid.org/0009-0003-8357-6418)
- Jinyi Li (ORCID: https://orcid.org/0000-0002-6324-9693)
- Yi Lu (ORCID: https://orcid.org/0009-0000-7094-6561)
- Shirong Wang (ORCID: https://orcid.org/0000-0002-6396-8292)
- Guan-lian Mo
- Tian-ming Gan
Institutions
- Guilin Medical University (CN)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2025-12-01
- DOI
- https://doi.org/10.3389/fcvm.2025.1689607
- Primary Topic
- Gout, Hyperuricemia, Uric Acid
- Type
- article
- Field-Weighted Citation Impact
- 0.00