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.

To explore the construction of a machine learning prediction model for CP risk among high-risk CVD populations.

Data from 5 666 participants in Nanjing, collected between September 2023 and August 2025 as part of the "Jiangsu Province Early Screening and Comprehensive Intervention Program for High-Risk Cardiovascular Populations" were analyzed. The dataset was randomly divided into training and testing sets at a 7∶3 ratio. Variables selected via Least Absolute Shrinkage and Selection Operator (LASSO) regression were used as independent variables, with CP as the outcome. Logistic Regression (LR), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Elastic Net (EN), and Support Vector Machine (SVM) models were constructed. Model performance was comprehensively evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score on the test set. The Shapley Additive Explanations (SHAP) method was employed to interpret the best-performing model.

A total of 5 666 participants were included, comprising 3 005 males (53.0%) and 2 661 females (47.0%). There were 2 027 (35.8%) in the normal group and 3 639 (64.2%) in the CP group. LASSO regression identified 21 key variables with non-zero coefficients, including sex, region, electrocardiogram results, age, serum uric acid, estimated glomerular filtration rate, glycated hemoglobin A_1c (HbA_1c), 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, education level, current occupation, smoking status, alcohol consumption, physical activity, and sleep quality. Among the models, XGBoost demonstrated the best overall predictive performance in the training set (AUC=0.747, accuracy=0.693, sensitivity=0.732, specificity=0.623, F1 score=0.754). SHAP analysis ranked the feature importance as follows: age, Hcy, CK-MB, region, HbA_1c, ApoB, and sex.

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.

心血管疾病（CVD）是全球人口发病和死亡的首要原因，颈动脉斑块（CP）作为动脉粥样硬化的重要标志物可用于预测心血管事件，但传统超声筛查在大规模人群中受限，探索机器学习预测模型有助于实现CP的早期识别与CVD防控。

探索基于CVD高危人群构建CP风险的机器学习预测模型。

选取"江苏省心血管病高危人群早期筛查与综合干预项目"中南京市2023年9月—2025年8月5 666例研究对象的数据，按7∶3比例随机划分训练集与测试集，将最小绝对收缩与选择算子回归（LASSO）筛选得到的变量作为自变量，以CP为结局，分别构建Logistic回归（LR）、类别提升（CatBoost）、极限梯度提升（XGBoost）、弹性网络（EN）、支持向量机（SVM）模型。基于测试集受试者工作特征曲线下面积（AUC）、准确度、灵敏度、特异度、F1分数综合评估不同模型效能，使用Shapley加法解释（SHAP）对最佳模型进行可解释性分析。

纳入5 666例研究对象中男3 005例（53.0%）、女2 661例（47.0%），正常组2 027例（35.8%）、CP组3 639例（64.2%）。通过LASSO回归筛选出21个非零系数关键变量，包括性别、地区、心电图、年龄、血尿酸、估计肾小球滤过率、糖化血红蛋白（HbA_1c）、同型半胱氨酸（Hcy）、非高密度脂蛋白胆固醇、载脂蛋白B（ApoB）、肌酸激酶同工酶（CK-MB）、舒张压、脉压、尿酸碱度、尿比重、受教育程度、当前职业、吸烟情况、饮酒情况、体力活动及睡眠质量。训练集中，XGBoost模型综合预测效能最优（AUC=0.747，准确度=0.693，灵敏度=0.732，特异度=0.623，F1分数=0.754）。基于SHAP的重要性排序结果依次为：年龄、Hcy、CK-MB、地区、HbA_1c、ApoB和性别。

基于XGBoost机器学习算法构建的CVD高危人群CP风险预测模型性能最优，可为基层医疗卫生机构提供简便、低成本的工具，用于早期识别CP个体，并有助于在动脉粥样硬化早期阶段进行干预，从而降低CVD不良结局的发生风险。