Open AccessHeart Disease Prediction using Machine Learning Models
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.e1013.0285s20
Subject(s) - logistic regression , random forest , decision tree , machine learning , artificial intelligence , support vector machine , classifier (uml) , logistic model tree , computer science , heart disease , decision tree learning , medicine
Healthcare has become one of the most important concerns in the world. The cases of heart disease are increasing on a rapid scale among the people especially among the young generation. We can save the lives of the people if we could detect the heart disease on/before time, by getting them treated. In this matter artificial intelligence can be of a great help. Here we have collected a data set and then we have built a prediction model to detect heart disease based on the various algorithms that are available for machine learning.we have used Logistic regression, K-NN, SVM, Decision Tree, Random Forest with the accuracy values of K-Neighbors Classifier (0.956194%), Support Vector Machine (0.9561945%), Decision Tree (0.91050%), Random Forest Classifier (0.95404%) and Logistic Regression (0.95592%). The best value given by the Machine Learning model is by Logistic regression followed by K-NN.