RULE BASED APRIORI APPROACH FOR HEART DISEASE PREDICTION

The work on diverse forms of data-mining techniques is used for the prediction of severe problems of the heart disease. It is usually done by the use of different data-mining tools. Apriori Rule base is useful in many domains playing a prime role in clinical research methods where the algorithms for Apriori Rule base processes are preferred to extract the knowledge i.e. hidden in the data-set. The scientifically discovered patterns helps in aiding the process of decision making which helps in saving the human lives facing the problem of heart disease. Health care organizations are undergoing major challenges leading to catastrophic results that are unacceptable in nature. The bad decisions related to health may cause death of human. In order achieve a cost-effective and corrective treatment, a good methodology should be adopted. The traditional methods of medical system reveals failed results and does not reveal the actual cause of pain. The main motive of this thesis is to design a system that helps in determining and extracting the knowledge i.e. hidden from the past patient’s records. It further detects the heart disease and allows the practitioners to take accurate decisions. To overcome these challenges decision support system and computer based information system must be used for better precise results. The process of heart abnormality prediction involves the use of a distinct form of technique known as Apriori Rule base. Relying on data-mining techniques, the process of mining has dedicated its career in planning of how to draw conclusions from diverse form of information