Machine Learning Using for Classification of Heart Failure

--------------------------------------------------------***--------------------------------------------------------Abstract Physicians classify patients into those with or without a specific disease. Classification trees are frequently used to classify patients according to the presence or absence of a disease. In the data-mining and machine learning, alternate classification schemes have been developed. These include Regression Tree, Random Forest, Boosting and Support Vector Machines (SVM). To analyze the heart failure, Regression Tree and SVM methods are compared in this paper. In Regression Tree method, it takes 30 sec to evaluate the result accurately and in SVM Learning Optimization, the result is evaluated in less than 30 sec. This paper shows that out of these two classification models SVM predicts heart disease with highest accuracy.