Retraction: Prediction of Heart Disease Using Machine Learning

Abstract

We want to give adherence to situ to identify the symptoms of heart disease in the first stage and stop it, given the increased increase in stroke rate at the tender level. It's funny for the average man to show the more expensive electrocardiogram questions every day. Because of this, there should be a favorable consensus in the area at a consistent time when the risk of heart disease is predicted. For this reason, we want to create an Assistant in the nursing framework that can predict the risk of heart disease based on key indicators such as age, gender, and heart rate. Neural codes for learning neural codes are well tested to be the most reliable and robust, and as a result, included in the predicted correlation.