JSEA  Vol.6 No.7 , July 2013
Fuzzy Rule Generation for Diagnosis of Coronary Heart Disease Risk Using Substractive Clustering Method
ABSTRACT

Fuzzy modeling techniques have been widely used to solve the uncertainty problems. A diagnosis of coronary heart disease (CHD) consists of some parameters numerical value of lingustics data. It can be implemented using fuzzy system through construction of the rules which relate to the data. However, the range of linguistics value is determined by an expert that depends on his knowledge to interpret the problem. Therefore, we propose to generate the rules automatically from the data collection using subtractive clustering and fuzzy inference Tagaki Sugeno Kang orde-1 method. The subtractive clustering method is a clustering algorithm to look for data clusters that serve as the fuzzy rules for diagnosis of CHD risk. The selected cluster number is determined based on the value of variant boundaries. Hence, it is applied to fuzzy inference system method, Takagi Sugeno Kang order-1, which determines diagnnosis of the desease. The advantage of this method is applicable to generate the fuzzy rules without defining and describing from an expert.


Cite this paper
L. Muflikhah, Y. Wahyuningsih and M.  , "Fuzzy Rule Generation for Diagnosis of Coronary Heart Disease Risk Using Substractive Clustering Method," Journal of Software Engineering and Applications, Vol. 6 No. 7, 2013, pp. 372-378. doi: 10.4236/jsea.2013.67046.
References
[1]   A. Priyono, M. Ridwan and A. Jais Alias, “Generation of Fuzzy Rules with Subtractive Clustering,” Jurnal Teknologi, Vol. 43, 2005, pp. 143-153. doi:10.11113/jt.v43.782

[2]   R. Arapoglou, K. Kolomvatsos and S. Hadjiefthymiades, “Buyer Agent Decision Process Based on Automatic Fuzzy Rules Generation Methods,” IEEE International Conference on Fuzzy Systems (FUZZ), Barcelona, 18-23 July 2010, pp. 1-8.

[3]   J. A. Dickerson and B. Kosko, “Fuzzy Function Approximation with Ellipsoidal Rules,” IEEE Transactions on System Man and Cybernetics, Vol. 26, No. 4, 1993, pp. 542-560.

[4]   L. T. Koczy, “Fuzzy Rule Base Model Identification Techniques,” Department of Telecommunication and Telematics, Budapest University of Technology and Economics, Budapest, 2002.

[5]   H. Salehfar, N. Bengiamin and J. Huang. “A Systematic Approach to Linguistic Fuzzy Modeling Based on InputOutput Data,” Proceedings of the 2000 Winter Simulation Conference, Orlando, 10-13 December 2000, pp. 480-486. doi:10.1109/WSC.2000.899755

[6]   P. R. J. Burch, “Coronary Disease: Risk Factors, Age, and Time,” American Heart Journal, Vol. 97, No. 4, 1979, pp. 415-419. doi:10.1016/0002-8703(79)90385-5

[7]   N. Allahverdi, S. Torun and I. Saritas, “Design of a Fuzzy Expert System for Determination of Coronary Heart Disease Risk and Technologies,” Computer Systems and Technologies 2006, Veliko Turnovo, 14-15 June 2007.

[8]   R. S. Sidhu, “A Subtractive Clustering Based Approach for Early Prediction of Fault Proneness in Software Modules,” World Academy of Science, Engineering and Technology 67, 2010.

[9]   S. Kusumadewi and H. Purnomo, “Fuzzy Logic Application for Decission Support System,” Graha Ilmu Press, Jakarta, 2010.

[10]   L. Man, T. C. Lim, S. Jian and L. Yue, “Supervised and Traditional Term Weighting Metodes for Automatic Text Categorization,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 4, 2009, pp. 721-735.

[11]   N. dan Gelley and R. Jang, “Fuzzy Logic Toolbox,” Mathwork, Inc., Natick, 2000.

[12]   K. H. Lee, “First Course on Theory and Applications,” Springer-Verlag Berlin Heidelberg, New York, 2005.

 
 
Top