Automated Cardiovascular Disease Detection from ECG Images Using Deep Learning

Healthcare is one of the crucial parts of our lives, and currently heart attack is one of the major causes of death worldwide. It is essential to predict the cardiovascular disease at an early stage. The traditional approach of consulting the physician periodically is neglected due to ignorance in the society. Automatic heart disease prediction and detection system would save several lives. The four types of cardiac abnormalities are NP, AH, MI, and HMI. To predict the cardiac abnormalities this paper uses the CNN technique of Deep Learning. The heart disease dataset of ‘UCI Cleveland’ is used in our proposed model. The dropout technique is used in our proposed model to avoid overfitting. For performance measurement, Various performance parameters like precision, accuracy, F1 score, and Recall are considered. The accuracy of our CNN model is 94%, which shows the significant achievement for the prediction.