Heart disease is most life-threatening disease in the world today. Diagnosing a patient with heart disease has turned
into a challenging work in the area of medical science. Huge amount of cardiovascular health study dataset collected from the
healthcare industries can be used to predict heart disease. In this work we compare different machine learning techniques such
as Naïve Bayes, Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbor (KNN), Random Forest and Artificial
Neural Network (ANN). This paper provides the overall information about the performance and accuracy of various machine
learning technique in heart disease prediction
