关键词: 心血管病, 颈动脉斑块, 机器学习, 预测模型

Abstract: Background　Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide. Carotid plaque (CP), as an important marker of atherosclerosis, can be used to predict cardiovascular events. However, conventional ultrasound screening is limited in large-scale populations. Exploring machine learning–based prediction models may facilitate the early identification of CP and the prevention and control of CVD. Objective　To explore the construction of a machine learning prediction model for CP risk among high-risk CVD populations. Methods　Data were obtained from the Nanjing arm of the “Jiangsu Province Early Screening and Comprehensive Intervention Program for High‑Risk Cardiovascular Populations” from September 2023 to August 2025. Participants were randomly split 7:3 into training and test sets. With CP as the outcome, we constructed logistic regression (LR), Categorical Boosting (CatBoost), extreme gradient boosting (XGBoost), elastic net (EN), and support vector machine (SVM) models. Model performance on the test set was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score. Shapley additive explanations (SHAP) were applied to the best‑performing model for interpretability. Results　Among the 5,666 participants, 3,639 (64.2%) were classified into the plaque group. Least absolute shrinkage and selection operator (LASSO) regression identified 21 key variables with non-zero coefficients, including sex, region, electrocardiogram findings, age, serum uric acid, estimated glomerular filtration rate, glycated hemoglobin (HbA1c), homocysteine (Hcy), non-high-density lipoprotein cholesterol, apolipoprotein B (ApoB), creatine kinase-MB (CK-MB), diastolic blood pressure, pulse pressure, urine pH, urine specific gravity, educational level, current occupation, smoking status, alcohol consumption, physical activity, and sleep quality. In the training set, the XGBoost model demonstrated the best overall predictive performance (AUC=0.747, accuracy=0.693, sensitivity=0.732, specificity=0.623, F1 score=0.754). According to SHAP importance ranking, the top predictors were age, Hcy, CK-MB, region, HbA1c, ApoB, and sex.Conclusions　The CP risk prediction model based on the XGBoost machine learning algorithm showed the best performance among high-risk CVD populations. This model may provide a simple and low-cost tool for primary healthcare institutions to facilitate early identification of individuals with CP, support timely intervention at the early stage of atherosclerosis, and help reduce the risk of adverse CVD outcomes.

Key words: Cardiovascular disease, Carotid plaque, Machine learning, Prediction model