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

Authors: K. Srinivas; G. Raghavendra Rao; A. Govardhan

Addresses: Jyothishmathi Institute of Technology and Science, Nustulapur, Karimnagar – 505 481, Andhra Pradesh, India ' Department of Computer Science and Engineering, National Institute of Engineering, Opposite silk factory, Mysore – 8, India ' School of Information Technology, JNTUH University, Kukatpally, Hyderabad – 85, India

Abstract: 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.

Keywords: class imbalance; FCM; rough sets; fuzzy classifiers; fuzzy logic; heart disease prediction; accuracy; fuzzy c-means clustering; heart diseases; cardiovascular diseases.

DOI: 10.1504/IJMEI.2014.065427

International Journal of Medical Engineering and Informatics, 2014 Vol.6 No.4, pp.297 - 318

Received: 25 May 2013
Accepted: 06 Dec 2013

Published online: 31 Oct 2014 *

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