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A Diagnostic Fuzzy Rule-Based System for Congenital Heart Disease

Authors: Ersin Kaya, Bulent Oran, Ahmet Arslan

Abstract:

In this study, fuzzy rule-based classifier is used for the diagnosis of congenital heart disease. Congenital heart diseases are defined as structural or functional heart disease. Medical data sets were obtained from Pediatric Cardiology Department at Selcuk University, from years 2000 to 2003. Firstly, fuzzy rules were generated by using medical data. Then the weights of fuzzy rules were calculated. Two different reasoning methods as “weighted vote method" and “singles winner method" were used in this study. The results of fuzzy classifiers were compared.

Keywords: Congenital heart disease, Fuzzy rule-basedclassifiers, Classification

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1062470

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[1] A. Dragulescu ,LL. Mertens. "Developments in echocardiographic techniques for the evaluation of ventricular function in children," Arch Cardiovasc Dis, vol. 103, pp. 603-614, Nov. 2010.
[2] H. Feigenbaum, "Role of M-mode technique in today's echocardiography," J Am Soc Echocardiogr, vol. 23, pp. 240-257, Mar. 2010
[3] A. Silvia, R. Richard, K.W. Simon, R.A. Juan, "A fuzzy system for helping medical diagnosis of malformations of cortical development," Journal of Biomedical Informatics, vol. 40, pp. 221-235, June 2007.
[4] L. Stavros, M. Ludmil, "Evolving fuzzy medical diagnosis of Pima Indians diabetes and of dermatological diseases" Artificial Intelligence in Medicine, vol. 50, pp. 117-126 Oct. 2010.
[5] K. Ali, K. Ayt├╝rk, Y. Ugur, "Expert system based on neuro-fuzzy rules for diagnosis breast cancer," Expert Systems with Applications, vol. 38, pp. 5716-5726, May. 2011.
[6] H. Ishibuchi, N. Yamamoto, "Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining," Fuzzy sets and systems, vol. 141, pp. 59-88, Jan. 2004.
[7] H. Ishibuchi, N. Yamamoto, "Rule weight specification in fuzzy rulebased classification systems," IEEE Trans. Fuzzy Systems, vol. 13, pp. 428-435, Aug. 2005.
[8] O. Cordon, M.J. del Jesus, F. Herrera, "A proposal on reasoning methods in fuzzy rule-based classification systems," International Journal of Approximate Reasoning, vol. 20, pp. 21-45, Jan 1999.
[9] H. Ishibuchi, T. Nakashima, T. Morisawa, "Voting in fuzzy rule-based systems or pattern classification problems," Fuzzy Sets and Systems, vol. 103, pp. 223-238, Apr. 1999.
[10] M.Z. Jahromi, M. Taheri, "A proposed method for learning rule weights in fuzzy rule-based classification systems," Fuzzy Sets and Systems, vol. 159, pp. 449-459, Feb.2008.
[11] H. Ishibuchi, N. Yamamoto, T. Nakashima, "Fuzzy data mining: effect of fuzzy discretization," in Proc. 1st IEEE International Conference on Data Mining, San Jose, CA, USA, 2001 pp. 241-248.