Open AccessPrediction of Heart Disease using Machine Learning
Author(s) -
Ankit Singh
Publication year - 2020
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit206415
Subject(s) - decision tree , naive bayes classifier , support vector machine , random forest , machine learning , artificial intelligence , logistic regression , computer science , bayes error rate , mean squared error , heart disease , classifier (uml) , statistics , bayes classifier , mathematics , medicine
Cardiovascular Disease is the leading cause of death (Approximately, 17 million people every year) in the all the area of the world. Prediction of heart disease is the critical challenge in the area of the clinical data analysis. The objective of paper is to build the model for predicting the Heart Disease using various machine learning classification algorithm. Classification is a powerful machine learning technique that is commonly used for prediction. Some of the classification algorithm are Logistic Regression, Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest Classifier, KNN. This paper investigate which algorithm is used for the improving the accuracy in the prediction of heart disease. And, a comparative analysis on the accuracy and mean squared error is to done for predicting the best model. The result of the study indicates that KNN algorithm is effective in predicting the model with the accuracy of the 85.71% and having a very low mean squared error.