HEART DISEASE PREDICTION









Abstract

Millions of deaths occur worldwide because of heart diseases per annum. Prediction and diagnosis of the diseases related to the heart require more accuracy, precision, and faultlessness, as the slightest mistake can cause various problems like fatigue and even result in the death of the person. In our project, we predict the chance of having heart disease by checking the accuracy of machine learning algorithms. For this the algorithms are Logistic Regression, K-Nearest Neighbor (KNN), Random Forest by using the UCI repository dataset for training and testing. Our aim is to seek out an appropriate machine learning technique that’s efficient likewise as accurate for the prediction of heart condition.


Modules


Algorithms


Software And Hardware