An XGBoost machine learning model demonstrated high accuracy for assessing coronary heart disease risk in Chinese postmenopausal women, achieving an AUC of 0.891 in the validation set.
Observational (n=376)
Yes
Does a machine learning-based model accurately assess coronary heart disease risk in Chinese postmenopausal women?
An XGBoost machine learning model accurately assesses coronary heart disease risk in Chinese postmenopausal women, potentially enabling early identification of high-risk patients.
Effect estimate: AUC 0.891
p-value: p=<0.05
OBJECTIVES: To investigate the risk factors of coronary heart disease (CHD) and develop a risk assessment model for CHD in postmenopausal women. METHODS: =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: <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.
Deng et al. (2026) conducted an observational in Coronary heart disease (n=376). Machine learning-based risk assessment models (XGBoost) was evaluated on Model performance (AUC) for CHD risk assessment (AUC 0.891, p=<0.05). An XGBoost machine learning model demonstrated high accuracy for assessing coronary heart disease risk in Chinese postmenopausal women, achieving an AUC of 0.891 in the validation set.