Review
Open access
Aug 2026
Prediction of carotid vulnerable plaques in patients with type 2 diabetes using interpretable machine learning models
An interpretable XGBoost model based on six clinical, laboratory, and CGM-derived indicators showed acceptable discrimination, calibration, and clinical net benefit for estimating ultrasound-defined plaque vulnerability among patients with T2DM and established carotid plaque.
Qi Cheng, Feng-Juan Zhang, Han Wang et al.
· Frontiers in Endocrinology · 0 citations