Analysis and Classification of Heart Disease and COVID 19 Using Electrocardiogram and Deep Learning

The health industry has access to a significant amount of data. All this data is frequently overlooked and underutilized. Many diseases can be diagnosed, prevented, or cured by employing such a large amount of data. Diseases such as cardiovascular diseases, cancer, tumors, Alzheimer's disease, and heart diseases pose a significant threat to humanity, claiming an estimated 17.9 million lives each year. Since its identification in December 2019, COVID-19 has become a pandemic. COVID-19-related deaths are continuing to rise, and early detection is becoming increasingly important. Utilizing ECG copies and deep learning algorithms, an ideal way for automatically diagnosing Corona virus and irregular heartbeats is proposed. ECGs are segmented. Each segment of the ECG is an attribute, and the photos constitute a dataset. Following that, the resulting scanned copies of ECG are given as input into a Convolutional Neural Network (CNN). Using deep learning system and ECG data, it will be possible to identify cardiovascular changes automatically. This along with the appearance of cardiovascular changes due to abnormalities in the heart but also recognizes impact made by COVID-19. This also suggests that the DL model could be used to detect COVID-19 and heart diseases. The proposed study, we hope, might provide a significant decision-making framework for healthcare practitioners.