Prediction of Heart Diseases Using Associative Classification

Today's health-care services have come a long way to provide medical care to the patients and protect them from various diseases. This paper comprises the development of a framework based on associative classification techniques on heart dataset for early diagnosis of heart based diseases. It is hard to diagnose the heart diseases with just observation that arrives suddenly and may prove fatal when it's uncontrolled. The implementation of work is done on Cleveland heart diseases dataset from the University of California Irvine (UCI) machine learning repository to test on different data mining techniques. The various attributes related to cause of heart diseases are viz: gender, age, chest pain type, blood pressure, blood sugar etc that can predict early symptoms heart disease. Various data mining algorithms such as Aprior, FP-Growth, Naive bayes, ZeroR, OneR, J48 and k-nearest neighbor are applied in this study for prediction of heart diseases. On basis of best results the development of heart disease prediction system is done by using hybrid technique for classification associative rules (CARs) to achieve the prediction accuracy of 99.19%.