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

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

Abstract Background Early detection of vulnerable carotid plaques is critical for stroke prevention. This study aimed to develop a machine learning model based on routine blood tests and derived indices to predict plaque vulnerability and assess sex-specific risk patterns across biomarker value rang...

Full description

Saved in:
Bibliographic Details
Main Authors: Yimin E, Zhichao Yao, Maolin Ge, Guijun Huo, Jian Huang, Yao Tang, Zhanao Liu, Ziyi Tan, Yuqi Zeng, Junjie Cao, Dayong Zhou
Format: Article
Language:English
Published: BMC 2025-08-01
Series:Cardiovascular Diabetology
Subjects:
Vulnerable carotid plaque
Machine learning
Blood biomarkers
SHAP analysis
Sex differences
Online Access:https://doi.org/10.1186/s12933-025-02867-6
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1186/s12933-025-02867-6

Similar Items