DOI: 10.21276/sjet

Pages: 427-439

Data mining is the process of automatically extracting knowledgeable information from huge amounts of data. It has become increasingly important as real life data enormously increasing. Data mining is an integral part of KDD, which consists of series of transformation steps from preprocessing of data to post processing of data mining results. The basic functionality of data mining involves classification, association and clustering. Classification is a pervasive problem that encompasses many diverse applications. To improve medical decision data mining techniques have been applied to variety of medical domains. A major challenge that many of the health care organizations are facing is the provision for lack of quality services like diagnosing patients correctly and administering treatment at reasonable costs. Data mining techniques answer several important and critical questions related to health care. We propose an approach to predict the heart diseases using data mining techniques. In this paper, we investigate on K-Means Clustering and illustrate a reliable prediction methodology to diagnose tuberculosis disease and classify between different stages of tuberculosis using Adaptive Neuro Fuzzy Inference System (ANFIS). This prediction model helps the doctors in efficient heart disease diagnosis process with fewer attributes. Heart disease is the most common contributor of mortality in India.