Deep learning-based CAD diagnosis using CNNs

Abstract

Coronary Artery Disease (CAD) remains a significant global health concern, necessitating accurate diagnostic methods. In this study, we propose a deep learning solution for CAD diagnosis, driven by the limitations of traditional Machine Learning (ML) techniques in effectively handling numerical data. To address this, we focus exclusively on numerical features and employ essential preprocessing steps, including converting nominal features to numerical representations, normalizing numeric values, and balancing the dataset. Subsequently, we evaluate three deep learning classifiers—Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM)—to achieve improved diagnostic accuracy. Our evaluation of the proposed methods using real data demonstrates the superiority of deep learning techniques compared to other common classifiers, such as Random Forests, Bagging, Decision Trees, and Support Vector Machines (SVM). CNNs excel in feature extraction, capturing intricate patterns associated with CAD. Although ANNs and LSTMs are valuable, they do not match the discriminative power of CNNs in this context. In summary, our study underscores the pivotal role of CNNs in CAD diagnosis, achieving a highest accuracy of 98.64 %, representing a notable improvement compared to the best results reported in previous studies. This research not only advances the scientific understanding of CAD diagnostics but also has the potential to significantly enhance clinical practice by providing more accurate and timely diagnoses, ultimately improving patient outcomes and reducing healthcare costs.