Adapting rough-fuzzy classifier to solve class imbalance problem in heart disease prediction using FCM

The main objective of this research is to develop a heart disease prediction technique by solving class imbalance problem. Class imbalance problem severely affects the performance of the prediction if the distribution of data is not clearly defined. To overcome class imbalance problem and achieve promising results in this work, the proposed technique is divided into three steps. Initially, the input data is given to fuzzy c–means clustering algorithm that converts the original data into equal number samples for all the classes. Then, rules are generated from the rough set theory and these rules are used for prediction with the fuzzy classifier. For testing, test data is converted into relevant space after matching with the original cluster centres and then, sample is tested with rough–fuzzy classifier. The results prove that the proposed technique generated excellent results by achieving the accuracy of 81% in Cleveland and 80% in Hungarian datasets.

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