From Type-I to Type-II Fuzzy System Modeling for Diagnosis of Hepatitis
Hepatitis is one of the most common and dangerous diseases that affects humankind, and exposes millions of people to serious health risks every year. Diagnosis of Hepatitis has always been a challenge for physicians. This paper presents an effective method for diagnosis of hepatitis based on interval Type-II fuzzy. This proposed system includes three steps: pre-processing (feature selection), Type-I and Type-II fuzzy classification, and system evaluation. KNN-FD feature selection is used as the preprocessing step in order to exclude irrelevant features and to improve classification performance and efficiency in generating the classification model. In the fuzzy classification step, an “indirect approach” is used for fuzzy system modeling by implementing the exponential compactness and separation index for determining the number of rules in the fuzzy clustering approach. Therefore, we first proposed a Type-I fuzzy system that had an accuracy of approximately 90.9%. In the proposed system, the process of diagnosis faces vagueness and uncertainty in the final decision. Thus, the imprecise knowledge was managed by using interval Type-II fuzzy logic. The results that were obtained show that interval Type-II fuzzy has the ability to diagnose hepatitis with an average accuracy of 93.94%. The classification accuracy obtained is the highest one reached thus far. The aforementioned rate of accuracy demonstrates that the Type-II fuzzy system has a better performance in comparison to Type-I and indicates a higher capability of Type-II fuzzy system for modeling uncertainty.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1125323Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1089
 K. Polat and S. Güneş, "Hepatitis disease diagnosis using a new hybrid system based on feature selection (FS) and artificial immune recognition system with fuzzy resource allocation," Digital Signal Processing, vol. 16, no. 6, 2006, pp. 889-901.
 "The Nemours Foundation/KidsHealth ", (Online). Available: http://kidshealth.org/parent/infections/bacterial_viral/hepatitis.html. (Accessed 31 Dec 2015).
 E. Dogantekin, A. Dogantekin and D. Avci, "Automatic hepatitis diagnosis system based on Linear Discriminant Analysis and Adaptive Network based on Fuzzy Inference System," Expert Systems with Applications, vol. 36, no. 8, 2009, pp. 11282-11286.
 L. A. Zadeh, "Fuzzy sets," Information and Control, vol. 8, 1965, pp. 338-353.
 L. A. Zadeh, "The concept of a linguistic variable and its application to approximate reasoning," Information Sciences, vol. 8, 1975, pp. 199-249.
 J.M. Mendel, "Uncertain Rule-Based Fuzzy Logic Systems:Introduction and New Directions," Prentice Hall, 2001.
 D. Wu and . J. M. Mendel, "Uncertainty measures for interval type-2 fuzzy sets," Information Sciences, vol. 177, 2007, pp. 5378-5393.
 J.M. Mendel, J. Robert and L. Feilong , "Interval type-2 fuzzy logic systems made simple," Fuzzy Systems, IEEE Transactions, vol. 14, no. 6, 2006, pp. 808-821.
 M.H Fazel Zarandi, M. Zarinbal and M. Izadi, "Systematic image processing for diagnosing brain tumors: A Type-I I fuzzy expert system approach," Applied soft computing, vol. 11, no. 1, 2011, pp. 285-294.
 J.M. Mendel and Robert I. Bob John. "Type-2 fuzzy sets made simple." Fuzzy Systems, IEEE Transactions on, vol. 10, no. 2, 2002, pp. 117-127.
 D. Çalişir and E. Dogantekin, "A new intelligent hepatitis diagnosis system: PCA-LSSVM," Expert Systems with Applications, vol. 38, no. 8, 2011, pp. 10705-10708.
 C. L. Blake and C. J. Merz, (1996). UCI repository of machine learning databases (Online) Available: http://www.ics.uci.edu./~mlearn/MLReporsitory.html.
 Ö. Uncu and I. B. Türkşen, "A novel feature selection approach: combining feature wrappers and filters," Information Sciences, vol. 177, no. 2, 2007, pp. 449-466.
 M.H Fazel Zarandi,M.R Faraji and M. Karbasian. "An exponential cluster validity index for fuzzy clustering with crisp and fuzzy data." Sci. Iran. Trans. E: Ind. Eng, vol. 17, no. 2, 2010, pp. 95-110.
 D. Nauck and K. Rudolf , "Obtaining interpretable fuzzy classification rules from medical data," Artificial intelligence in medicine, vol. 16, no. 2, 1999, pp. 149-169.
 L. A. Zadeh, "Toward extended fuzzy logic—A first step," Fuzzy Sets and Systems, vol. 160, 2009, pp. 3175-3181.
 L. Qilian and J. M. Mendel, "Interval type-2 fuzzy logic systems:theory and design," IEEE Transactions on Fuzzy Systems, vol. 8,2000, pp. 535-550.
 "Canadian Liver Foundation (CLF)," (Online). Available: http://www.liver.ca/liver-disease. (Accessed 31 Dec 2015).
 W. Duch, K. Grudzinski, and G. Diercksen. "Neural minimal distance methods." In Proc. 3-rd Conf. on Neural Networks and Their Applications, Kule, Poland, 1997, pp. 183-188.
 W. Duch and Karol Grudziński. "Ensembles of similarity-based models." In Intelligent Information Systems, 2001, pp. 75-85.
 W. Duch and R. Adamczak. "Statistical methods for construction of neural networks." In ICONIP, 1998, pp. 639-642.
 W. Duch, R. Adamczak, and Diercksen, “Neural Networks from Similarity Based Perspective.” New Frontiers in Computational Intelligence and its Applications. Ed. M. Mohammadian, IOS Press, Amsterdam, 2000, pp.93-108.
 B. Ster, and Andrej Dobnikar. "Neural networks in medical diagnosis: Comparison with other methods." Proceedings of the International Conference EANN. vol. 96. 1996, pp. 427-430.
 N. Jankowski. "Approximation and classification in medicine with incnet neural networks." Machine Learning and Applications. Workshop on Machine Learning in Medical Applications, 1999, pp. 53-58.
 L. Ozyilmaz and Y. Tulay "Artificial neural networks for diagnosis of hepatitis disease." In Neural Networks, 2003. Proceedings of the International Joint Conference on, vol. 1, 2003, pp. 586-589.
 R.A. Vural, L. Özyılmaz, and T. Yıldırım. "A comparative study on computerised diagnostic performance of hepatitis disease using ANNs." In Computational Intelligence, 2006, pp. 1177-1182.