Predicting Heart Attack

Abstract

Heart diseases are increasing day by day, and predicting these diseases is very important and worrying. This diagnosis is a difficult task because it must be done accurately and efficiently. Most studies focus on patients at risk for heart disease based on a variety of medical conditions. We proposed a heart disease predictor that uses the patient's medical history to predict whether a patient will be diagnosed with heart disease. We use various machine learning algorithms, such as logistic regression and KNN, to predict and classify heart patients. A useful way to formalize how the model can be used to improve the accuracy of heart attack prediction for each individual The power of the model is very good and can predict the evidence of heart disease in an individual using KNN and logistic regression, comparing performance with other methods and previously used classifiers such as Naive Bayes. By using the given model to find the probability of the distributor being correct and to determine the heart disease, we have taken most of the pressure off ourselves. Predicting heart disease improves health and reduces costs. This project has given us important information that can help us predict patients with heart disease