Abstract:
The global burden of death from heart attacks has increased dramatically in the
modern era. South Asians are more likely than those in other parts of the world to get
a heart attack at a young age. Being able to accurately and rapidly forecast the stage
of a heart attack patient requires extensive expertise as well as a deep level of
understanding. The medical industry has access to a great quantity of data that may be
utilized to make informed judgments thanks to all the concealed information. We will
be able to predict heart attack patients' states or stages rapidly with good judgment
and a few excellent data mining methods like logistic regression and decision trees.
Support vector machine (SVM), random forest classifier, decision tree, logistic
regression, KNN, and Gaussian Naive Bayes are the six algorithms we employed in
our system (GaussianNB). The accuracy of our final model, which applies the SVM
method, is 92%.
