Development and validation of a machine learning model for predicting vulnerable carotid plaques using routine blood biomarkers and derived indicators: insights into sex-related risk patterns
A Random Forest model integrating routine blood markers and derived indices accurately predicted vulnerable carotid plaques. The results underscore the importance of sex-specific risk assessment, highlighting differential effects of key biomarkers across genders and value intervals.
Authors
- Jian Huang (ORCID: https://orcid.org/0000-0002-3931-5013)
- Zhanao Liu (ORCID: https://orcid.org/0000-0002-7521-819X)
- Yuqi Zeng (ORCID: https://orcid.org/0000-0001-9882-9156)
- Guijun Huo (ORCID: https://orcid.org/0009-0002-9141-7786)
- Dayong Zhou (ORCID: https://orcid.org/0000-0001-7443-5638)
- E Yimin
- Yaoliang Tang (ORCID: https://orcid.org/0000-0002-8372-1545)
- Ziyi Tan (ORCID: https://orcid.org/0000-0002-9536-9112)
- Maolin Ge (ORCID: https://orcid.org/0000-0002-8803-1463)
- Junjie Cao (ORCID: https://orcid.org/0000-0002-1161-4775)
- Zhichao Yao (ORCID: https://orcid.org/0009-0006-6236-248X)
Institutions
- Suzhou Municipal Hospital (CN)
- Northern Jiangsu People's Hospital (CN)
Publication Details
- Journal
- Cardiovascular Diabetology
- Published
- 2025-08-10
- DOI
- https://doi.org/10.1186/s12933-025-02867-6
- Citations
- 11
- Primary Topic
- Cardiovascular Health and Disease Prevention
- Type
- article
- Field-Weighted Citation Impact
- 15.27
Funders
- National Science and Technology Major Project