Prediction of Cardiac Disorder using ML Technology

Abstract

ML is a useful tool in the healthcare industry since it can determine whether locomotor disorders, heart ailments, and other conditions will exist or not. This project uses ML methods including logistic regression, backward elimination, and REFCV to estimate a person's risk of acquiring heart disease using a readily available Kaggle dataset. After that, a confusion matrix and cross validation are used to assess the outcomes. Early diagnosis of cardiovascular conditions can assist patients in altering their lifestyles to reduce the risk of complications, which may represent a significant medical advance.