The heart is the next big organ with more importance in the human body relative to the brain, which pumps the blood and supplies it to all organs of the whole body. Prediction of occurrences of heart diseases in the medical field is significant work. Data analytics is helpful for prediction from more information, and it helps the medical centre predict various diseases. In this paper, different techniques of mining for forecasting heart risk discussed. Heart disease cause millions of death every year, it is rapidly increasing mining methods one too much helpful detect and diagnose heart risk. Different mining methods have used to abstract knowledge for forecasting heart disease. In this paper, the survey is carried on various single data mining techniques to achieve high accuracy in predicting heart disease. Here we use many classifications, namely Random Forest Classifier (RFC), K-Nearest Neighbor Classifier (KNN), Gradient Boosting Classifier (GBC), Extra Tree Classifier (ETC), Extreme Gradient Boosting Classifier (XGB), the approach of classifiers. Analysis of various methods proved that techniques based on classification obtain high accuracy compared to previous methods. The performance of the classifier model is confirmed to outperform its counterparts progressively. The improved accuracy of various classifiers experimented in this reported research work vouches for its application in Heart Disease classification (HD).