Comparative analysis of machine learning models for coronary artery disease prediction with optimized feature selection.

Background: Coronary artery disease (CAD) is a major global cause of death, necessitating early, accurate prediction for better management. Traditional diagnostics are often invasive, costly, and less accessible. Machine learning (ML) offers a non-invasive alternative, but high-dimensional data and redundancy can hinder performance. This study integrates Bald Eagle Search Optimization (BESO) for feature selection to improve CAD classification using multiple ML models.

Methods: Two publicly available datasets, Framingham (4200 instances, 15 features) and Z-Alizadeh Sani (304 instances, 55 features), were used. The former predicts 10-year CAD risk, while the latter classifies current CAD status. Data preprocessing included missing value imputation, normalization, categorical encoding, and class balancing using SMOTE. We employed a 70-30 holdout validation strategy with empirical hyperparameter optimization, providing more reliable final model development than cross-validation. BESO was applied to optimize feature selection, significantly outperforming traditional methods like RFE and LASSO. Six ML models-KNN, logistic regression, SVM with linear, polynomial, and RBF kernels, and random forest-were trained and evaluated.

Results: Random Forest achieved the highest performance across both datasets. In the Framingham dataset, RF recorded 90 % accuracy, significantly outperforming traditional clinical risk scores (71-73 % accuracy). Linear models performed better on the Z-Alizadeh Sani dataset (90 % accuracy) than Framingham (66 %), indicating dataset characteristics strongly influence model efficacy.

Conclusion: BESO significantly enhances feature selection, with RF emerging as the optimal classifier (92 % accuracy) and substantially outperforming established clinical risk scores. This study highlights the potential of AI-driven CAD diagnosis, supporting early detection and improved patient outcomes. Future work should focus on prospective validation and clinical implementation.