A Novel Frequent Features Prediction Model for Heart Disease Diagnosis

Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease patients. Decision Tree is one of the data mining techniques used in the diagnosis of heart disease showing considerable success. It is essential to find the best fit classification algorithm that has greater accuracy on classification in the case of heart disease prediction. Since the data is huge attribute selection method used for reducing the dataset. Then the reduced data is given to the classification. We also propose a novel feature selection method algorithm which is the Attribute Selected Classifier method combining CFS subset evaluator and Best First method followed by J48 Decision tree then integrating the repetitive Maximal Frequent Pattern. The proposed algorithm provides better accuracy compared to the traditional algorithm and the hybrid Algorithm CFS. This research paper proposed a Novel frequent feature selection method for Heart Disease Prediction. Good performance of this method comes from the use of the Repetitive Maximal Frequent Pattern Method and the nonadditivity of the method against different target nominal attributes measure reflects the importance of the feature attributes as well as their interactions.