Regression based model for prediction of heart disease recumbent

Abstract

Supervised Learning, a novel method that figures out how to anticipate the resultant of an input-output pair by inducting data under series of training and testing functions. Regression model is a sub classification of Supervised Machine Learning. In this paper various Regression models such as Logistic Regression, SVM, KNN, Naive Bayes and Random forest have been applied on Heart Disease dataset. The anticipated outcomes draw the deduction on the level of patients inclined to coronary illness dependent on the traits and qualities. In reference to the applied calculations both KNN and Random Forest beats the other relapse calculation with a precision of 88.52%.