Application of an interpretable machine learning model based on optimal feature selection for predicting triple-vessel coronary disease: a multicenter retrospective study
RCII, as a composite biomarker integrating lipid metabolism and chronic inflammation, demonstrates strong predictive utility in identifying individuals at high risk for triple-vessel coronary disease.
Authors
- Jinbo Zhao
- Ke Su (ORCID: https://orcid.org/0000-0001-9503-7137)
- Ke He (ORCID: https://orcid.org/0000-0001-6446-9439)
- Ling Hou (ORCID: https://orcid.org/0009-0009-2922-6656)
- Changjiang Zhang (ORCID: https://orcid.org/0000-0002-1443-2010)
Institutions
- The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture (CN)
- Minda Hospital (CN)
Publication Details
- Journal
- PeerJ
- Published
- 2025-12-09
- DOI
- https://doi.org/10.7717/peerj.20423
- Primary Topic
- Cardiovascular Disease and Adiposity
- Type
- article
- Field-Weighted Citation Impact
- 0.00