Arrythmia Disease Prediction using Deep Learning Techniques

Abstract

The major goal of this study is to put into practise a straightforward algorithm to identify the illness arrhythmia. Any issue with a person's heartbeat's pace or rhythm is referred to as arrhythmia, which is a sickness. The heart cannot adequately pump blood when it is not beating appropriately. The danger that the organs may shut down or suffer damage is very great when this occurs, making it impossible for the lungs, brain, and all other organs to function properly. Arrhythmias are frequently thought to be unimportant and untreated. The doctor once stated that if someone develops an arrhythmia, they should determine whether it is abnormal or only interferes with the heart's normal function. The results of the ECG report are typically examined by the doctors to determine the heartbeat rate. Maximum accuracy in identifying the disease is possible with deep learning. The model is created as the source data are used to train it and build the model. These data are used by the model to train itself, and it uses the previously learned data to forecast the occurrence of the disease. As a result, the disease's presence is established, and the model is supplied with data from the reports. Hence, by doing so, the likelihood of diagnosing errors and the amount of time needed to analyse the report are both significantly decreased.