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Explainable machine-learning model for coronary artery disease diagnosis in non-AMI patients: integrating
Kexin Yang1, Sheng Liu1, Chenyang Wang1
1Center for Coronary Heart Disease, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Insights
An interpretable machine learning model integrating inflammatory biomarkers and traditional risk factors improves coronary artery disease (CAD) diagnosis. Key predictors include hypertension, hyperlipidaemia, sex, triglycerides, and interleukin-6.
Area of Science:
- Cardiology
- Biomedical Informatics
- Machine Learning
Background:
- Coronary artery disease (CAD) diagnosis relies on traditional risk factors.
- Inflammatory biomarkers may offer additional diagnostic value.
- Explainable machine learning (ML) can integrate complex datasets for clinical insights.
Purpose of the Study:
- To develop and interpret an explainable ML model for CAD diagnosis.
- To integrate inflammatory biomarkers with traditional risk factors.
- To enhance early CAD detection and clinical decision-making.
Main Methods:
- Retrospective analysis of 4656 patients undergoing coronary angiography.
- Collected demographic, clinical, biochemical, and 12 inflammatory cytokine data.
- Employed Least Absolute Shrinkage and Selection Operator (LASSO) regression and 10 ML algorithms, with SHapley Additive exPlanations (SHAP) for interpretability.
Main Results:
- A generalised linear model achieved an Area Under the Curve (AUC) of 0.815.
- SHAP analysis identified hypertension, hyperlipidaemia, sex, triglycerides, and interleukin-6 as significant CAD predictors.
- The model demonstrated balanced performance metrics including accuracy, sensitivity, specificity, and F1 score.
Conclusions:
- Interpretable ML models combining inflammatory and traditional markers show promise for early CAD diagnosis.
- This approach can enhance clinical decision-making in cardiology.
- Explainable AI facilitates understanding of complex disease predictors.
Objective:
To develop and interpret an explainable machine learning (ML) model integrating inflammatory biomarkers and traditional risk factors for the diagnosis of coronary artery disease (CAD).
Methods:
We retrospectively analysed 4656 patients undergoing coronary angiography for suspected CAD. Demographic, clinical, biochemical and 12 inflammatory cytokine variables were collected. Least absolute shrinkage and selection operator regression identified 21 candidate features. 10 ML algorithms were trained using cross-validation. Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity and F1 score. Model explainability was assessed using SHapley Additive exPlanations (SHAP).
Results:
Among the evaluated models, the generalised linear model showed balanced performance and was selected as the final model (AUC=0.815). SHAP analysis identified hypertension, hyperlipidaemia, sex, triglycerides and interleukin-6 as the most influential predictors of CAD.
Conclusions:
These findings underscore the potential of interpretable ML approaches that combine inflammatory and traditional markers to enhance early CAD diagnosis and inform clinical decision-making.
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