The successful application of data mining in highly visible fields like e-business, commerce and trade has led to its application in other industries. The medical environment is still information rich but knowledge weak. There is a wealth of data possible within the medical systems. However, there is a lack of powerful analysis tools to identify hidden relationships and trends in data. Heart disease is a term that assigns to a large number of heath care conditions related to heart. These medical conditions describe the unexpected health conditions that directly control the heart and all its parts. Medical data mining techniques like association rule mining, classification, clustering is implemented to analyze the different kinds of heart based problems. Classification is an important problem in data mining. Given a database contain collection of records, each with a single class label, a classifier performs a brief and clear definition for each class that can be used to classify successive records . A number of popular classifiers construct decision trees to generate class models. The data classification is based on MAFIA algorithms which result in accuracy, the data is estimated using entropy based cross validations and partition techniques and the results are compared. C4.5 algorithm is used as the training algorithm to show rank of heart attack with the decision tree. The heart disease database is clustered using the K-means clustering algorithm, which will remove the data applicable to heart attack from the database.