Effective Detection of Heart Disease Symptoms using Machine Learning

Abstract

Cardiac disease is leading around the globe for deaths, although early detection and prevention can improve survival rates. Using machine learning, various Activation Functions create new models and make predictions using the data they collect. In previous studies, the detection of disease signs was accomplished through the application of machine learning algorithms. Several pieces of paper were examined. Dr. Mohan was able to predict heart disease by analyzing blood pressure. It was a supervised machine-learning technique that he called random forest. In this study, principal component analysis as well as five different techniques were used. Using the methods described above, we projected that the Random Forest technique would provide the highest accuracy.