Automated Detection of Heart Attacks: A CNN-Based Approach

Abstract

Abstract: Seventeen million people die because of the world’s cardiovascular diseases annually that make up to 32% of the global population. One fourth (25%) of global deaths and 17 million each year, representing almost a third (32%), are attributable to cardiovascular disease or the world heart attack syndrome. In addition, cardiovascular disease kills an amazing 32% of overall deaths on the globe per quarter of percentage to be exact. In the short term, heart disease’s mortality rate could decline by predicting it. It has demonstrated that convolutional neural network is the best approach when implementing deep learning, generating training and test accuracy of 94.2%. Deep learning algorithm comprises of artificial neuronal network that uses a supervised machine learning method for the data set prediction. The EGC can also be used to detect cardio diseases. Deep learning will turn out being the most preferred approach because of its high precision and predictive power which also translates to good sensitivity and specificity. Deep learning techniques give better results than others, for example, CNN with autoencoders.