Cardiovascular Disease Detection Using Deep Learning And Machine Learning In ECG Images

Abstract

One of the leading causes of death worldwide are cardiovascular diseases (heart diseases). The earlier they can be predicted and classified; the more lives can be saved. Electrocardiogram (ECG) is a common, inexpensive, and noninvasive tool for measuring the electrical activity of the heart and is used to detect cardiovascular disease. In this article, the power of deep learning techniques was used to predict the four major cardiac abnormalities: abnormal heartbeat, myocardial infarction, history of myocardial infarction, and normal person classes using the public ECG images dataset of cardiac patients. First, the transfer learning approach was investigated using the low-scale pretrained deep neural networks SqueezeNet and AlexNet. Second, a new convolutional neural network (CNN) architecture was proposed for cardiac abnormality prediction. Third, the aforementioned pretrained models and our proposed CNN model were used as feature extraction tools for traditional machine learning algorithms, namely support vector machine, K-nearest neighbors, decision tree, random forest, and Naïve Bayes. According to the experimental results, the performance metrics of the proposed CNN model outperform the exiting works; it achieves good accuracy, recall, precision, and F1 score. Moreover, when the proposed CNN model is used for feature extraction, it achieves the best score using the NB algorithm.