Using Differential Evolution Algorithm and Rough Set Theory to Reduce the Features of Cataract Disease in a Medical Diagnosis System

Rough set theory can deal with vagueness and uncertainty in data analysis, and can efficiently remove redundant information in many areas. There are many features that help to assessment of the diseases, also this data contain irrelevant features, while uncertainties and missing values also exist. In this paper, Differential Evolutionary (DE) has been used to reduce the features based on the rough set method on the some of the cataracts people. Differential evolution algorithm is a new heuristic approach that having many motivations for using, such finding the true global minimum of a multimodal search space with too many derivations that is very difficult to find, regardless of the initial parameter values, fast convergence rate, and need to tuning a few control parameters. Since the act of choosing the distinguishing features have an unsparing efficiency on the optimized and speedy assessment of diseases, using a convergence and fast algorithm such DE has been suggested. Cite this work as: Raheleh Maleki, Vahideh Keikha, and Hassan Rezaei, "Using Differential Evolution Algorithm and Rough Set Theory to Reduce the Features of Cataract Disease in a Medical Diagnosis System," TSEST Transaction on Electrical and Electronic Circuits and Systems, Vol. 3(5), Pp. 22-25, Aug., 2013. References: [1]    Pawlak, Z. and R. Slowinski, Rough set approach to multi –attribute decision analysis, European Journal of Operational Research 72, 443-449, 1994. [2]    Pawlak, Z, Rough sets and intelligent data analysis, Information Science 147, pp.1-12, 2002. [3]    Pawlak, Z., Rough sets, J. Comput. Information Science 11, 341-345, 1982. [4]    Storn, R., Price, K., Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces, Technical Report TR-95-012, Berkeley, CA, 1995. [5]    Zhao, Y., Y. Yao , Luo, F., Data analysis based on discernibility and indiscernibility, Information Science, Volume 177, Issue 22, 4959-4979, 2007. [6]    Subudhi, B., Jena, D., An Improved Differential Evolution Trained Neural Network Scheme for Nonlinear System Identication, International Journal of Automation and Computing, 6(2), 137-144, DOI: 10.1007/s11633-009-0137-0, 2009. [7]    Krink, T., Filipic, B., Fogel, and G, Noisy Optimization Problems – A Particular Challenge for Differential Evolution. 2004 Congress on Evolutionary Computation, 332-339, 2004. [8]    Kromer, P., Snasel, V., Platos , J., Abraham, A.: Optimization of Turbo Codes by Differential Evolution and Genetic Algorithms, Ninth International Conference on Hybrid Intelligent Systems 2009 IEEE, DOI 10.1109/HIS.289, 2009. [9]    Small, R. L., Wendel,J. F., Differential Evolutionary Dynamics of Duplicated Paralogous Adh Loci in Allotetraploid Cotton (Gossypium), by the Society for Molecular Biology and Evolution. ISSN: 0737-4038, 2002. [10]  Babu, B., Jehan, M., Differential Evolution for Multi-Objective Optimization. In: the 2003 Congress on Evolutionary Computation, Vol. 4 2696-2703, 2003. [11]  Biswas, R. and Nanda. S, Rough groups and rough subgroups, Bulletin of the Polish Academy of Sciences Mathematics, vol. 42, no. 3, pp. 251-254, 1994. [12]  Rakus- Andersson, Elisabeth, Fuzzy and Rough Techniques in Medical Diagnosis and Medication, Springer, 937349, 2007. [13]  Sanchez, Eigen Fuzzy Sets and Fuzzy Relation, Journal of Mathematical Analysis And applications 81 ,399-421, 1981. [14]  Sanches, Resolution of Eigen Fuzzy Set Equation, Fuzzy Sett and Systems 1,69-74, 1978. [15]  Sanchez, Medical diagnosis and composite fuzzy relations”, advance in fuzzy set theory and application ,437---444, 1979. [16]  Feoktistov, V., Janaqi, S, Generalization of the Strategies in Differential Evolution. Proceedings of the 18th International Parallel and Distributed Processing Symposium,165-170, 2004. [17]  Omran, M .G.H, Salman, A, Engelbrecht, A. P., Self-adaptive Differential Evolution, CIS 2005, Part I, LNAI 3801, pp. ,192 – 199, 2005. [18]  Wanga, Yanga, Jensenb, Liua , Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma, computer methods and programs in biomedicine 8 3, 147–156, 2006. [19]  Zhang, W.-J., Xie, X.-F. ,DEPSO: Hybrid Particle Swarm with Differential Evolution Operator, IEEE International Conference on Systems, USA, 3816-3821, 2003.