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.<\/p>\r\n","references":"[1] C. Zhang, S. Zhang, Association Rule Mining. Berlin: Springer-Verlag,\r\n2002, ch. 1.\r\n[2] S. Pal, P. Mitra, Pattern Recognition Algorithms for Data Mining, Boca\r\nRaton: Chapman Hall, Boca Raton, 2004, ch.1.\r\n[3] H. S. Nguyen, D. Slezak, \"Approximate reducts and association rules\r\ncorrespondence and complexity results\", Lecture Notes in Computer\r\nScience, vol. 1711, pp. 137-145, November 1999.\r\n[4] Z. Pawlak, \"Rough sets and intelligent data analysis\", Information\r\nSciences, vol. 147, pp. 1-12, November 2002.\r\n[5] J. Komorowski, L. Polkowski, A. Skowron. (1998). Rough sets: a\r\ntutorial (Online). Available:\r\nhttp:\/\/citeseer.ist.psu.edu\/komorowski98rough.html\r\n[6] S. Hui. (2002), Rough set classification of gene expression data\r\n(Online). Available:\r\nhttp:\/\/www.cs.uwaterloo.ca\/~s2hui\/RoughSetProject.pdf\r\n[7] J. Komorowski, Z. Pawlak, L. Polkowski. (1999). A rough set\r\nperspective on data and knowledge (Online). Available:\r\nhttp:\/\/citeseer.ist.psu.edu\/336073.html\r\n[8] H. S. Binay, \"Rough set approaches in investment decisions\", Ph.D.\r\ndissertation, Dept. Business Adm., Ankara Univ., Ankara, 2002.\r\n[9] Intelligent Decision Support: Handbook of Advance and Applications of\r\nthe Rough Set Theory\", Kluwer Academic Publishers, 1992, pp, 311-\r\n362.\r\n[10] J. M. Saquer, \"Formal concept analysis and applications\", Ph.D.\r\ndissertation, Dept. Comp. Sci. and Eng. Univ. of Nebraska, Lincoln,\r\n2000.\r\n[11] J. M. Saquer, J. S. Deogun, \"Concept approximations based on rough\r\nsets and similarity measures\", Int. Journal App. Math. Computer\r\nScience, vol. 11, pp. 655-674, 2001.\r\n[12] J. S. Deogun, J. M. Saquer, \"Monotone concepts for formal concept\r\nanalysis\", Discrete Applied Mathematics, vol. 144, pp. 70-78.\r\n[13] W. Ganter, R. Wille, Formal Concept Analysis: Mathematical\r\nFoundation. Berlin: Springer-Verlag, 2002, ch.1.\r\n[14] J. S. Deogun, V. V. Raghavan, H. Sever, \"Association mining and\r\nformal concept analysis\", in Proc. 6th Int. Workshop on Rough Set,\r\nData Mining and Granular Computing\", North Carolina, 1998, pp. 335-\r\n338.\r\n[15] B. O\u2500\u0192uz, H. Sever, M. Tolun, \"E\u253c\u0192le\u253c\u0192tirme Sorgular\u2500\u2592n\u2500\u2592n Modellenmesi\",\r\nin The 9th Turkish Symposium on Artificial Intelligence and Neural\r\nNetworks\", Izmir, 2000, pp. 381-390.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 11, 2007"}