Application of Deep Learning Technique for the Detection of Coronary Heart Disease (CHD)

Abstract

A blockage or cessation of blood flow to our heart muscle is a sign of Coronary Heart Disease (CHD), which is caused by an accumulation of fatty substances in the coronary arteries known as atheroma. The principal blood channels that deliver blood to the heart are known as coronary arteries. In this paper, the possible application of deep learning technique is explored for the detection of Coronary Heart Disease. In this study, the methodology includes infrastructure creation for the detection of CHD using supervised and unsupervised learning. The result shows the proceeding to manage the data set and process the data there, user input is appropriately gathered. The embedded system that conducts data translation is linked to the controller sensor, which regulates the CHD pulse system. The study concludes that the diagnostic accuracy of the deep learning model using Decision Tree (DT) and Naive Bayes (NB) Classification is effective and error-free. DT and NB Classification is an effective way of detecting CHD by the deep learning model. The future potential of this paper is the development of models for heart disease diagnoses in patients around the world.