Medical diligences generate a huge amount of data. Cardiovascular Disease is becoming a common disease leads to
death now a days. Hence it is important to predict as early as possible. Heart disease data are stored in a database in large
amount. Different Machine Learning techniques (SVM, Logistic Regression, Neural Network, KNN, RF, Naïve Bayes, DT, and
GDBT- Bagging Tree) can be used to classify the data in the database. This paper concentrates on various ML techniques that
are used to predict the heart disease by using dataset. The accuracy, sensitivity, specificity and Area under Curve (AUC) are
calculated for various techniques
