Abstract
Heart disease is becoming the biggest cause of mortality worldwide. Its early detection can considerably lower the risk of mortality and help to promote its successful treatment. However, this early detection necessitates regular monitoring of a wide range of clinical and lifestyle factors. This is why a growing number of studies are being conducted to automate the forecasting of cardiac diseases, beginning with an examination of ECG images, which is the first diagnostic test performed on patients and also the most simple and economical to conduct. This study investigates the use of three groups of ECG images acquired from three separate sets of cardiac patients, with different heart-related illnesses, and a set of healthy controls to predict heart disease using deep learning classifiers. The evaluation is carried out on a real-life dataset, and the results highlight really interesting findings.
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