Cardiovascular Abnormalities Prediction using CNN and EfficientNetB3 model
Abstract
In recent days cardiovascular diseases are more dangerous and to be monitored early for human’s health monitoring. Cardiovascular Diseases (CVD) causes many million deaths throughout the world. An Irregular pattern variation in ECG signals, like too quickly or too slowly, is known as arrhythmia. The main aim of historical research in ECG classification is identifying arrhythmia. Electrocardiogram (ECG) gives information about heart activity of the person. ECG signal has P,Q,R,S,T,U sample points that shows heart activity of the person. Machine learning and deep learning algorithms recently used for various medical health disorders prediction. This research proposes a model that predicts heart diseases using machine learning and deep learning. Impulsive cardiac Death (ISD) is known as unexpected cardiovascular disease-related deaths with dates and times which are not predicted early.The proposed research uses efficient Net-B3 and Convolutional neural network(CNN) for prediction of Cardiovascular disease prediction.This research compares CNN and EfficientNet-B3 model to predict heart diseases and EfficientNet-B3 model produces highest accuracy 95% when compared to basic CNN.