Myocardial Prediction and Identification using Convolution Neural Networks

Heart disease is considered as one of the major diseases which have been increasing due to modern lifestyle and it has become one of the factors of death as a deadly disease. There is a more sensitive disease to explore and we are on the edge and moving forward to gain the knowledge and explore it. There is humongous research and data about healthcare. Therefore, by using and examining new and appreciable techniques can make or predict the defect of a being who can be affected with the diseases related to heart diseases and can help in preventing and treating them in the early stages. In this research, we suggest a solution for them based on Machine Learning (ML) and Data Mining (DM) approaches, which has proven to be beneficial in the medical field. The goal of this study is to look at risk factors that lead to harmful consequences such as heart disease, as well as novel ways for detecting, predicting, and preventing heart disease, as well as overcoming the limitations of previous research. The article we submitted is a suggestion for method called Cardio plus, which incorporates a machine learning algorithm called (CNN) convolutional neural network to predict the likelihood of cardiovascular illness in patients. The suggested technique is concerned with temporal data modeling, and it makes use of CNN for HF prediction.