Classification Algorithms for Predicting Heart Diseases and their Accuracies

This project has been undertaken to detect the presence of heart disease in patients by taking some parameters like age, serum cholesterol level, resting blood pressure etc. as input and we have used 4 classification algorithms namely support vector machine, k-nearest neighbor, decision tree and linear regression. In this paper, we calculated the accuracy of machine learning algorithms for predicting heart disease. We have trained the system by using a heart disease dataset from the UCI repository and taken some real-time data for testing purposes. For the implementation, we used Python programming Anaconda (Jupiter) notebook and Django-a high-level Python web framework used in rapid development, for developing our project. We have successfully developed a model to detect the presence of heart disease and calculated the accuracy of all the algorithms used.