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Coronary artery disease prediction using Bayesian-optimized support vector machine with feature selection
Abdul Zahir Baratpur1, Hamed Vahdat-Nejad1, Emrah Arslan2
1Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
Insights
This study introduces a non-invasive framework for predicting Coronary Artery Disease (CAD) using machine learning, achieving high accuracy and providing interpretable results for clinical use.
Area of Science:
- Cardiovascular research
- Medical informatics
- Machine learning in healthcare
Background:
- Coronary Artery Disease (CAD) is a major global health concern.
- Current diagnostic methods like invasive angiography are costly and carry risks.
- There is a need for non-invasive, accurate, and interpretable CAD prediction tools.
Purpose of the Study:
- To develop and validate a non-invasive, interpretable framework for Coronary Artery Disease (CAD) prediction.
- To leverage machine learning for enhanced CAD risk stratification.
- To identify clinically relevant features for CAD prediction.
Main Methods:
- Utilized the Z-Alizadeh Sani dataset for model development.
- Employed a hybrid decision tree-AdaBoost for feature selection (30 features).
- Applied SMOTE oversampling within cross-validation folds to prevent data leakage.
- Optimized Support Vector Machine (SVM) using Bayesian hyperparameter tuning.
- Interpreted model predictions using SHapley Additive exPlanations (SHAP).
Main Results:
- The SVM_Bayesian model achieved 97.67% accuracy, 100% sensitivity, and 99% AUC.
- Outperformed logistic regression, random forest, standard SVM, and SLOA-optimized SVM.
- SHAP analysis identified key predictors like Typical Chest Pain and Age.
- Statistical tests and temporal generalization confirmed model robustness.
Conclusions:
- The proposed framework offers a transparent, generalizable, and clinically actionable tool for CAD risk stratification.
- Demonstrates the potential of interpretable machine learning in cardiovascular disease prediction.
- Highlights the importance of interconnected cardiovascular features in systemic disease prediction.
Introduction:
Cardiovascular diseases, particularly Coronary Artery Disease (CAD), remain a leading cause of mortality worldwide. Invasive angiography, while accurate, is costly and risky. This study proposes a non-invasive, interpretable CAD prediction framework using the Z-Alizadeh Sani dataset.
Methods:
A hybrid decision tree-AdaBoost method is employed to select 30 clinically relevant features. To prevent data leakage, SMOTE oversampling is applied exclusively within each training fold of a 10-fold cross-validation pipeline. The Support Vector Machine (SVM) model is optimized using Bayesian hyperparameter tuning and compared against Sea Lion Optimization Algorithm (SLOA) and grid search. SHapley Additive exPlanations (SHAP) analysis is utilized to interpret the feature contributions.
Results:
The SVM_Bayesian model achieves 97.67% accuracy, 95.45% precision, 100.00% sensitivity, 97.67% F1-score, and 99.00% AUC, outperforming logistic regression (93.02% accuracy, 92.68% F1-score), random forest (95.45% accuracy, 93.33% F1-score), standard SVM (77.00% accuracy), and SLOA-optimized SVM (93.02% accuracy). Ablation studies and Wilcoxon signed-rank tests confirm the statistical superiority of the proposed model.
Discussion:
SHAP analysis reveals clinically meaningful feature contributions (e.g., Typical Chest Pain, Age, EFTTE). 95% bootstrap confidence intervals and temporal generalization on an independent test set ensure robustness and prevent overfitting. Future work includes validation on external real-world datasets. This framework provides a transparent, generalizable, and clinically actionable tool for CAD risk stratification, aligned with the principles of network physiology by focusing on interconnected cardiovascular features in predicting systemic disease.
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