Abstract
Intelligent automated decision support systems are found to be useful for early detection of hepatitis for augmenting survivability. We present here an intelligent system for hepatitis disease diagnosis using UCI data set for experiment. We use multiple imputation technique for managing missing values in the UCI data set. One of the potential tools in this context is neural network for classification. For better diagnostic classification accuracy, various feature selection techniques are deployed as prerequisite. These features are considered to be more informative to the doctors for taking final decision. This work attempts rough set-based feature selection (RS) technique. For classification, we use incremental back propagation learning network (IBPLN), and Levenberg-Marquardt (LM) classification tested on UCI data base. We compare classification results in terms of classification accuracy, specificity, sensitivity and receiver-operating characteristics curve area(AUC).
Keywords
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References
Polat, K., Gunes, S.: 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 16(6), 889–901 (2006)
Rezaee, K., et al.: An Intelligent Diagnostic System for Detection of Hepatitis using Multi-Layer Perceptron and Colonial Competitive Algorithm. The J. of Mathematics and Computer Science 4(2), 237–245 (2012)
Calisir, D., Dogantekin, E.: A new intelligent hepatitis diagnosis system: PCA-LSSVM. Expert Systems with Applications 38, 10705–10708 (2011)
Neshat, M., Sargolzaei, M., Nadjaran, A.N., Masoumi, A.: Hepatitis Disease Diagnosis using Hybrid Case Based Reasoning and Particle Swarm Optimization. ISRN Artificial Intelligence (2012), doi:10.5402/2012/609718
Honaker, J., King, G., Blackwell, M.: 2011, AMELIA II: A Program for Missing Data ( accessed: 3rd, November 2011), http://gking.harvard.edu/amelia/
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society. Series B 39(1), 1–38 (1977)
Honaker, J., King, G.: What to do About Missing Values in Time Series Cross-Section Data. American J. of Political Science 54(2), 561–581 (2010)
King, G., Tomaz, M., Wittenberg, J.: Making the Most of Statistical Analyses: Improving and Presentation. American Journal of Political Science 44(2), 341–355 (2000)
Pawlak, Z.: Rough sets. Int. J. of Parallel Programming 11(5), 341–356 (1982)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Klwer Academic Publishing, Dordrecht (1991)
McCulloch, W., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 7, 115–133 (1943)
Hebb, D.O.: The Organization of Behavior, a Neuropsychological Theory. John Wiley, New York (1949)
Roy, A.: Artificial Neural Networks- A Science in Trouble. SIGKDD Explorations 1(2), 33–38 (2000)
Rumelhart, D.E., McClelland, J.L. (eds.): Parallel Distributed Processing: Explorations in Microstructures of Cognition, vol. 1, pp. 318–362. Foundations, MIT Press, Cambridge (1986)
Rumelhart, D.E.: The Architecture of Mind: A Connectionist Approach. In: Haugeland, J. (ed.) Mind_design II, ch. 8, pp. 205–232. MIT Press (1986)
Grossberg, S.: Nonlinear Neural Networks: Principles, Mechanisms, and Architectures. Neural Networks 1, 17–61 (1988)
Moody, J., Darken, C.: Fast Learning in Networks of Locally-Tuned Processing Units. Neural Computation 1, 281–294 (1989)
Fu, L., Hsu, H., Principe, J.C.: Incremental Backpropagation Learning Networks. IEEE Trans. on Neural Networks 7(3), 757–761 (1996)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representation by error propagation. In: Parallel Distributed Processing: Explorations in the Microstructures of Cognition, vol. 1, MIT Press, MA (1986)
Levenberg, K.: A method for the solution of certain non-linear problems in least squares. Quarterly in Applied Mathematics 2(2), 164–168 (1944)
Marquardt, D.W.: An algorithm for the least-squares estimation of nonlinear parameters. SIAM Journal of Applied Mathematics 11(2), 431–441 (1963)
Amitava Basu, M.D.: Pathologist, India (personal communication)
Hall, E., Frank, G., Holmes, B., Pfahringer, P., Reutemann, I., Witten, H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)
Hung, M.S., Shankar, M., Hu, M.Y.: Estimating Breast Cancer Risks Using Neural Networks. J. Operational Research Society 52, 1–10 (2001)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximator. Neural Network 2, 359–366 (1991)
Goa, D.: On structures of supervised linear basis function feedforward three-layered neural networks. Chin. J. Comput. 21(1), 80–86 (1998)
Huang, M.L., Hung, Y.H., Chen, W.Y.: Neural network classifier with entropy based feature selection on breast cancer diagnosis. J. Med. Syst. 34(5), 865–873 (2010)
Alyuda NeuroIntelligence 2.2, http://www.alyuda.com
Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7), 1145–1159 (1997)
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Mitra, M., Samanta, R.K. (2015). Hepatitis Disease Diagnosis Using Multiple Imputation and Neural Network with Rough Set Feature Reduction. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_31
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DOI: https://doi.org/10.1007/978-3-319-11933-5_31
Publisher Name: Springer, Cham
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