A COMPARATIVE STUDY OF VARIOUS ML TECHNIQUES FOR HEART DISEASE PREDICTION

Abstract

Heart is one in every of the most important organs that has a lot of precedence in flesh. It provides the blood to any or all organs of the entire body by pumping it. Heart condition may be a prime root of death within the world. A large quantity of information is collected in medical business associated with heart condition. However, this knowledge isn't mined properly. Prediction of heart diseases in care field is critical work. Several researchers have already been operating within the field of heart condition prediction exploitation some machine learning algorithms. The results of analysis vary from dataset to dataset. Advanced machine learning models area unit accustomed discover data in information and for medical analysis, significantly in heart condition prediction. In this research, the analysis of predictive frameworks is finished for cardiopathy utilizing a bigger range of input attributes. We've applied the various ML classification techniques for the detection of similar designs and elements within the Cleveland information from the UCI Machine Learning Repository utilizing python information manipulation applications. For the prediction of the presence of cardiopathy in a person, we've applied six ML classification techniques specifically SVM, KNN, NB, RF, LR and DT.