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Modeling the Symptom-Disease Relationship by Using Rough Set Theory and Formal Concept Analysis

Authors: Mert Bal, Hayri Sever, Oya Kalıpsız

Abstract:

Medical Decision Support Systems (MDSSs) are sophisticated, intelligent systems that can provide inference due to lack of information and uncertainty. In such systems, to model the uncertainty various soft computing methods such as Bayesian networks, rough sets, artificial neural networks, fuzzy logic, inductive logic programming and genetic algorithms and hybrid methods that formed from the combination of the few mentioned methods are used. In this study, symptom-disease relationships are presented by a framework which is modeled with a formal concept analysis and theory, as diseases, objects and attributes of symptoms. After a concept lattice is formed, Bayes theorem can be used to determine the relationships between attributes and objects. A discernibility relation that forms the base of the rough sets can be applied to attribute data sets in order to reduce attributes and decrease the complexity of computation.

Keywords: Granular Computing, Rough Set Theory, Formal Concept Analysis, Medical Decision Support System

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1070163

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References:


[1] C. Zhang, S. Zhang, Association Rule Mining. Berlin: Springer-Verlag, 2002, ch. 1.
[2] S. Pal, P. Mitra, Pattern Recognition Algorithms for Data Mining, Boca Raton: Chapman Hall, Boca Raton, 2004, ch.1.
[3] H. S. Nguyen, D. Slezak, "Approximate reducts and association rules correspondence and complexity results", Lecture Notes in Computer Science, vol. 1711, pp. 137-145, November 1999.
[4] Z. Pawlak, "Rough sets and intelligent data analysis", Information Sciences, vol. 147, pp. 1-12, November 2002.
[5] J. Komorowski, L. Polkowski, A. Skowron. (1998). Rough sets: a tutorial (Online). Available: http://citeseer.ist.psu.edu/komorowski98rough.html
[6] S. Hui. (2002), Rough set classification of gene expression data (Online). Available: http://www.cs.uwaterloo.ca/~s2hui/RoughSetProject.pdf
[7] J. Komorowski, Z. Pawlak, L. Polkowski. (1999). A rough set perspective on data and knowledge (Online). Available: http://citeseer.ist.psu.edu/336073.html
[8] H. S. Binay, "Rough set approaches in investment decisions", Ph.D. dissertation, Dept. Business Adm., Ankara Univ., Ankara, 2002.
[9] Intelligent Decision Support: Handbook of Advance and Applications of the Rough Set Theory", Kluwer Academic Publishers, 1992, pp, 311- 362.
[10] J. M. Saquer, "Formal concept analysis and applications", Ph.D. dissertation, Dept. Comp. Sci. and Eng. Univ. of Nebraska, Lincoln, 2000.
[11] J. M. Saquer, J. S. Deogun, "Concept approximations based on rough sets and similarity measures", Int. Journal App. Math. Computer Science, vol. 11, pp. 655-674, 2001.
[12] J. S. Deogun, J. M. Saquer, "Monotone concepts for formal concept analysis", Discrete Applied Mathematics, vol. 144, pp. 70-78.
[13] W. Ganter, R. Wille, Formal Concept Analysis: Mathematical Foundation. Berlin: Springer-Verlag, 2002, ch.1.
[14] J. S. Deogun, V. V. Raghavan, H. Sever, "Association mining and formal concept analysis", in Proc. 6th Int. Workshop on Rough Set, Data Mining and Granular Computing", North Carolina, 1998, pp. 335- 338.
[15] B. Oğuz, H. Sever, M. Tolun, "Eşleştirme Sorgularının Modellenmesi", in The 9th Turkish Symposium on Artificial Intelligence and Neural Networks", Izmir, 2000, pp. 381-390.