Abstract:

The healthcare industry’s data generated has increased exponentially due to reforms in technology, use of IOT and intensive patient care. This necessitates use of data mining technology for efficient processing and decision making. The early detection and prediction of curable diseases in healthcare can be done via use of this intelligent tool. This paper compares use of different classifiers in data mining for predicting Angiographic disease status.

Keywords— Data Mining, Health informatics, Classifiers, Healthcare, Naïve bayes, Kstar, Random tree, Random Forest, Logistic model tree, AdaBoost, J48, ZeroR, Kmeans;