Paper Title
Heart Attack Prediction Using Ensemble Classifier

The heart attack prediction at a very early stage is crucial step which can be used to avoid its fatal effect. In this project we have improved the accuracy of heart attack prediction using ensemble classification. We have implemented two ensemble classification algorithm and one decision tree algorithm to show that which algorithm performs best in terms of accuracy as well as shows least relative and absolute error. We have then compared the result of all the three algorithms used on the dataset. The dataset has been selected from kaggle. [1], we have performed literature review to gain insight of all the algorithms used so far for early prediction of heart rate. From which we found that ensemble classification has been used widely to increase the accuracy of the system therefore we employed ensemble classification algorithm which increased the accuracy of our research. The main advantage of using ensemble classification as compared to other method is that it combines the prediction of two or more models. Keywords - Ensemble Classifier, Data Preprocessing ,Feature Extraction etc