Cognitive Approach for Heart Disease Prediction using Machine Learning

Prediction of patterns to prevent and control diseases is a challenging and a prominent requirement in medical domain. In this paper, we propose a machine learning framework to predict the possibility of having heart disease using various algorithms. The framework is executed using five algorithms Random Forest, Naïve Bayes, Support Vector Machine, Hoeffding Decision Tree, and Logistic Model Tree (LMT). Cleveland dataset is used for training and testing the model. The dataset is preprocessed followed by feature selection to select most prominent features. The resultant dataset is then used for training the framework. The results are combined and show that Random forest gives maximum accuracy.