Detection of Cardiovascular Diseases from ECG Images Using Machine Learning and Deep Learning Methods

Abstract

Abstract: Cardiovascular diseases (CVDs) are the leading cause of death globally, emphasizing the importance of early prediction and classification for saving lives. Electrocardiogram (ECG) is a widely used, cost-effective, and noninvasive tool for measuring heart electrical activity, crucial for detecting CVDs. This study leverages deep learning techniques to predict four major cardiac abnormalities—abnormal heartbeat, myocardial infarction, history of myocardial infarction, and normal individuals—using a public dataset of ECG images from cardiac patients. The research explores transfer learning with pretrained deep neural networks such as SqueezeNet and AlexNet, as well as introduces a novel convolutional neural network (CNN) architecture for cardiac abnormality prediction. Additionally, pretrained models and the new CNN architecture are utilized as feature extraction tools for traditional machine learning algorithms including support vector machine, K-nearest neighbors, decision tree, random forest, and Naïve Bayes. Experimental results showcase the superiority of the proposed CNN model over existing works, achieving 98.23% accuracy, 98.22% recall, 98.31% precision, and 98.21% F1 score. Furthermore, when the proposed CNN model is utilized for feature extraction, it achieves the highest score of 99.79% using the Naïve Bayes algorithm.