Abstract: The diagnosis of heart disease is a significant and tedious task in medicine. The healthcare environment is generally perceived as being information rich yet knowledge poor. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. Knowledge discovery and data mining have found numerous applications in business and scientific domain. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. Using medical profile such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established. It can serve a training tool to train nurses and medical students to diagnose patients with heart disease. It is a web based user friendly system and can be used in hospitals if they have a data ware house for their hospital. Presently we are analyzing the performances of the ten classification data mining techniques by using various performance measures. For implementation of the work a real time patient database is taken and the patient records are experimented and the final best classifier is identified with quick response time and least error rate. A typical confusion matrix is furthermore displayed for quick check. The study describes algorithmic discussion of the heart disease dataset from Cleveland Heart Disease database, on line repository of large datasets. The Best results are achieved by using Tanagra tool. Tanagra is data mining matching set. The accuracy is calculate based on addition of true positive and true negative followed by the division of all possibilities.