Heart Attack Analysis Detection System Using Machine Learning Methods

Abstract

In order to investigate Heart Attack Analysis and Detection Using Machine Learning Methods, models that predict the type of news in a new condition determined by using Machine Learning Models have been studied. In particular, the performance of various classifiers, including logistic regression, Knearest neighbour (KNN), support vector machine (SVM), Naive Bayes and decision tree, is compared. In the experiments using a real-life dataset, logistic regression and SVM gave the best results with a test accuracy of 90%. Naive Bayes achieved an accuracy of 86.67%, KNN 83.33% and decision tree 63.33%. These results suggest that logistic regression and SVM can be suitable and effective machine learning models for predicting heart attack risk