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Interpretable machine learning for coronary heart disease risk stratification in patients with carotid
Lei Zhang1,2, Mengke Lyu2, Mingyuan Du1,2
1Heart Center, The First Affiliated Hospital of Henan University of Chinese Medicine, National Regional (TCM) Cardiovascular Diagnosis and Treatment Center, Zhengzhou, Henan, China.
Insights
A new machine learning model accurately predicts coronary heart disease (CHD) risk in patients with carotid atherosclerosis. The interpretable logistic regression model uses key factors like age, diabetes, and carotid plaque for better clinical decisions.
Area of Science:
- Cardiovascular Medicine
- Medical Informatics
- Machine Learning in Healthcare
Background:
- Carotid atherosclerosis is a significant risk factor for coronary heart disease (CHD).
- Accurate risk stratification is crucial for timely intervention in patients with carotid atherosclerosis.
- Existing risk prediction models may not fully capture the complexity of CHD development in this population.
Purpose of the Study:
- To develop and validate a machine learning model for predicting CHD risk in patients with carotid atherosclerosis.
- To identify key predictors of CHD in this patient cohort.
- To assess the clinical utility and interpretability of the developed model.
Main Methods:
- Retrospective analysis of 442 patients with carotid atherosclerosis.
- Development and comparison of seven machine learning algorithms (Logistic Regression, XGBoost, LightGBM, Random Forest, KNN, SVM, Stacking Ensemble).
- Feature selection using logistic regression, identifying age, diabetes, hyperlipidemia, transient ischemic attack (TIA), and carotid plaque as predictors.
- Performance evaluation using 10-fold cross-validation, AUC, accuracy, sensitivity, specificity, and F1 score.
- Interpretability assessed with Shapley Additive Explanations (SHAP) and clinical utility via calibration and decision curve analysis.
Main Results:
- All machine learning models demonstrated satisfactory performance.
- The Logistic Regression (LR) model achieved the highest Area Under the Curve (AUC) of 0.838 on the testing set.
- SHAP analysis highlighted carotid plaque presence and TIA as the most influential predictors.
- Calibration and decision curve analysis confirmed strong agreement between predicted and observed risks, indicating significant clinical net benefit.
Conclusions:
- An interpretable Logistic Regression model incorporating age, diabetes, hyperlipidemia, TIA, and carotid plaque provides reliable CHD risk stratification for patients with carotid atherosclerosis.
- This model serves as a practical and explainable tool for individualized risk assessment.
- The model supports early clinical decision-making in this high-risk population.
Abstract:
This study aimed to develop and validate a machine learning model for risk stratification of coronary heart disease (CHD) in patients with carotid atherosclerosis, with CHD presence/absence defined as the target outcome variable. A retrospective analysis was conducted on 442 patients diagnosed with carotid atherosclerosis at a tertiary hospital in China between January 1, 2022, and June 20, 2025. Patients were divided into CHD and non-CHD groups based on clinical outcomes. Data encompassing demographics, laboratory results, and vascular imaging findings were collected. Feature selection involved logistic regression (LR), identifying 5 key predictors: age, diabetes, hyperlipidemia, transient ischemic attack (TIA), and the presence of carotid atherosclerotic plaque. Seven machine learning algorithms (LR, XGBoost, LightGBM, random forest, K-nearest neighbors, support vector machine, and stacking ensemble) were trained and evaluated. Model performance was assessed using 10-fold cross-validation, with metrics including area under the curve, accuracy, sensitivity, specificity, and F1 score. Model interpretability was evaluated using Shapley Additive Explanations, while clinical utility was determined through calibration and decision curve analysis. All models demonstrated satisfactory performance, with the LR model achieving the highest area under the curve of 0.838 on the testing set, indicating balanced sensitivity and specificity. Shapley Additive Explanations analysis identified carotid plaque and TIA as the most influential predictors. Calibration and decision curve analysis curves indicated strong agreement between predicted and observed risks, leading to a significant clinical net benefit. An interpretable LR model incorporating age, diabetes, hyperlipidemia, TIA, and carotid plaque enables reliable CHD risk stratification among patients with carotid atherosclerosis. This model serves as a practical, explainable tool for individualized risk assessment and early clinical decision support in this high-risk population.
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