Interactive Data Exploration is a key ingredient of a diverse set of discovery-oriented application. Data discovery is a highly ad hoc interactive process where it is difficult to retrieve relevant data from large, unorganized pools. In this project, the diseases can be predicted based on the analysis from their symptoms and the report is generated from the systematic analysis of particular disease. Early detection and prevention of diseases plays a very important role in reducing the mortality rate caused by those diseases. It is a multilayered method which uses significant pattern mining using iterative search techniques to build a risk prediction system which predicts various disease. It is user friendly, time and cost saving. This research uses data mining technology such as classification and prediction to identify potential treatments for patients according to their diseases. The gathered data is preprocessed, fed into the database and classified to yield significant patterns using pattern mining algorithm. Finally a prediction system is developed to analyze risk levels which help in prognosis. This helps in detection of a person’s predisposition for any common diseases before going for clinical and lab tests which is cost and time consuming.