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A Pragmatic Approach of Preprocessing the Data Set for Heart Disease Prediction

Dr. Durairaj.M, Sivagowry.S

Medical Ecosystem is originated with rich information database, but inadequate in techniques to extract the information from the database. This is because of the lack of effective analysis tool to discover hidden relationships and trends in them. By applying the data mining techniques, valuable knowledge can be extracted from the health care system. Extracted knowledge can be applied for the accurate diagnosis of disease and proper treatment. Heart disease is a group of condition affecting the structure and functions of the heart and has many root causes. Heart disease is the leading cause of death in all over the world over past ten years. Researchers have developed many hybrid data mining techniques for diagnosing heart disease. This paper describes a preprocessing technique and analyzes the accuracy for prediction after preprocessing the noisy data. It is also observed that the accuracy has been increased to 91% after preprocessing. Swarm Intelligence techniques hybrided with Rough Set Algorithm are to be taken as future work for exact reduction of relevant features for prediction.