Detection of Cardiovascular Diseases using Artificial Neural Networks

Abnormalities in normal blood flow from the heart cause several types of heart disease known as cardiovascular diseases. Over more than 17.5 million Global death occurs due to cardiovascular disease and 75% of death occurs from low- and middle-income countries. Early detection can save a lot of people’s lives. ECG signals reflect the electrical activity of the heart. Thus, heart rhythm disorders or alterations in the ECG waveform are evidence of underlying cardiovascular problems, such as arrhythmias. However, manual analysis of electrocardiogram (ECG) signals is a laborious and prone-to-error task, even for a specialist with many hours of experience. This work focuses on identifying the five diseases of a dataset with the help of an artificial neural network. We have used a sequential model in keras with 5 dense layers. Then the ANN keras model is trained from the selected parameters of dataset and then test dataset is used. The classification report tells us how well the model has identified the diseases