OBJECTIVES: To investigate the risk factors of coronary heart disease (CHD) and develop a risk assessment model for CHD in postmenopausal women. METHODS: General information, medical history, and laboratory test results of the patients were collected from postmenopausal women with CHD admitted to two medical centers in Yangzhou (Jiangsu Province, China) from November, 2018 to November, 2023. After excluding cases with incomplete medical records, 1197 patients were included, who were divided into the training cohort (n=821) and validation cohort (n=376) based on the hospital of admission. In the training cohort, the risk factors for CHD in postmenopausal women were identified using Lasso regression, multivariate logistic regression analysis, and machine learning algorithms including Light GradientBoosting Machine (LGBM), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Naive Bayes (NB). Risk assessment models were constructed using these algorithms, and their performance was evaluated using ROC curves, decision curve analysis (DCA), and calibration curves. RESULTS: Lasso regression suggested body mass index (BMI) classification and glycated hemoglobin were independent risk factors for CHD in postmenopausal women, whereas age at menopause and high-density lipoprotein cholesterol (HDL-C) were independent protective factors (P<0.05). Among the machine learning models, XGBoost demonstrated the best assessment performance in both the training set (AUC: 0.912; sensitivity: 0.892; specificity: 0.766; recall: 0.892; F1-score: 0.899) and the validation set (AUC: 0.891; sensitivity: 0.836; specificity: 0.921; recall: 0.837; F1-score: 0.877). Calibration curve and DCA curve analyses indicated good consistency between the predicted and actual outcomes. A nomogram and SHAP summary plot were used to visualize and interpret the logistic regression model and the XGBoost model, respectively. CONCLUSIONS: The risk assessment model for CHD in Chinese postmenopausal women established in this study demonstrates good accuracy and applicability to allow early identification of high-risk patients. 目的: 研究影响绝经女性冠心病发生的潜在危险因素，构建和验证绝经女性冠心病发生的评估工具。方法: 本研究选取2018年11月~2023年11月期间，入院于江苏省扬州市两家大型医疗中心的绝经后冠心病（CHD）的女性患者。收集患者的一般信息、既往病史及相关实验室检查结果。排除病历不完整的病例后，共纳入1197例患者。根据入院医院，将患者分为训练队列（n=821）和验证队列（n=376）。在训练集内，利用Lasso回归、多因素逻辑回归分析以及多种机器学习算法，包括Light Gradient Boosting Machine（LGBM）、随机森林、决策树、支持向量机、极端梯度提升（XGBoost）、K最近邻和朴素贝叶斯，识别绝经后女性冠心病的风险因素。基于此构建了风险评估模型，并通过ROC曲线、决策曲线分析（DCA）及校准曲线评估模型的性能及适用性。结果: Lasso回归分析显示，BMI分级和糖化血红蛋白是绝经后女性冠心病的独立风险因素，而绝经年龄和高密度脂蛋白胆固醇为独立保护因素（P<0.05）。在比较的各类机器学习模型中，XGBoost模型在训练集（AUC:0.912;敏感性:0.892;特异性:0.766;召回率:0.892;F1分数:0.899）和验证集（AUC:0.891;敏感性:0.836;特异性:0.921;召回率:0.837;F1分数:0.877）均表现出最佳评估性能。校准曲线和决策曲线显示预测结果与实际结果之间具有良好的一致性及临床适用性。此外，利用列线图和SHAP总结图分别对逻辑回归模型和XGBoost模型进行了可视化和解释。结论: 本研究通过回顾性分析建立了针对中国绝经后女性的冠心病风险评估工具，该工具具有较好的准确性和适用性，有助于早期识别高风险患者并进行干预。.