An Effectuation Analysis of Heart Attack Prediction Using Machine Learning Algorithms

Abstract

The Machine Learning field turns out consecutive statistics and artificial intelligence denomination. A sub sector of artificial intelligence is machine learning. Machine Learning can promote the treatment procedure by growing patient involvement and so fetching excellent health outcomes. Machine Learning models can qualify the motive explanation of all receivable results for the similar patient and it can raise the diagnostic accuracy of every gradation. Heart attack is more common in people over the age 65 and are more likely to occur as people get older. For predicting heart attack various machine learning algorithms are being used. Such as Logistic Regression, Naive Bayes, Decision Tree, Random Forest and SVM KNN. For all of these algorithms I found the best accuracy in Naive Bayes. Confusion matrix is being used for all classification and shows better outcomes.