A Diagnostic Fuzzy Rule-Based System for Congenital Heart Disease
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.
[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.
[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.
@article{"International Journal of Medical, Medicine and Health Sciences:55289", author = "Ersin Kaya and Bulent Oran and Ahmet Arslan", title = "A Diagnostic Fuzzy Rule-Based System for Congenital Heart Disease", 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", volume = "5", number = "6", pages = "237-4", }
