VAE-CNN for Coronary Artery Disease Prediction

Coronary artery disease is a nuanced cardiovascular condition that is intricate and multifactorial, denoted by the constriction of coronary arteries resulting in compromised blood supply to the cardiac muscle. It necessitates a thorough strategy for diagnosis, management, and prevention, emphasizing lifestyle adjustments and medicinal measures to alleviate the impact of the condition and enhance the well-being of patients. As a result, there is an urgent imperative for the development of cost-effective, automated diagnostic technologies geared towards early CAD detection. Such advancements are crucial for enabling proactive management of chronic cardiovascular conditions within healthcare systems. The rise of machine learning (ML) applications in the medical domain holds immense promise, offering the capability to unravel complex patterns within extensive and diverse medical datasets. By integrating ML methodologies for CAD classification, there lies the potential to alleviate diagnostic uncertainties and improve clinical decision-making processes. This research endeavors to construct a machine learning-driven system tailored specifically for CAD detection. Through meticulous analysis of patient medical records, ML algorithms aim to forecast the likelihood of CAD development in individuals, thereby facilitating timely interventions and risk mitigation strategies. Furthermore, this study seeks to elucidate the pivotal risk factors underlying CAD manifestation, thereby enhancing our comprehension of disease etiology and informing targeted preventive measures. To achieve these objectives, a prominent DL classifier, namely CNN, is deployed to discern patterns within the Z-Alizadeh Sani dataset—a representative repository of real-world patient health records. To address inherent data imbalances, the VAE algorithm is employed to bolster minority class instances. Additionally, model hyperparameters are optimized using the grid search hyperparameter tuning. The resulting framework showcases promising efficacy, with the Catboost classifier, in conjunction with VAE, demonstrating notable accuracy compared to alternative classifiers. Rigorous evaluation employing diverse metrics—including accuracy, recall, F-score, precision, and receiver operating characteristic (ROC) curve analysis—attests to the robustness and generalization capacity of the proposed model.