Abstract
This paper presents an intelligent diagnostic supporting system – i + DiaKAW (Intelligent and Interactive Diagnostic Knowledge Acquisition Workbench), which automatically extracts useful knowledge from massive medical data to support real medical diagnosis. In which, our two novel pre-processing algorithms MIDCA (Multivariate Interdependent Discretization for Continuous-valued Attributes) and LUIFS (Latent Utility of Irrelevant Feature Selection) for continuous feature discretization (CFD) and feature selection (FS) respectively, assist in accelerating the diagnostic accuracy by taking the attributes’ supportive relevance into consideration during the data preparation process. Such strategy minimizes the information lost and maximizes the intelligence and accuracy of the system. The empirical results on several real-life datasets from UCI repository demonstrate the goodness of our diagnostic system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cios, K.J., Moore, G.W.: Uniqueness of Medical Data Mining. Journal of Artificial Intelligence in Medicine 26(1), 1–24 (2002)
Yu, L., Liu, H.: Efficient Feature Selection via Analysis of Relevance and Redundancy. Journal of Machine Learning Research 5, 1205–1224 (2004)
Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Caruana, R., Sa, V.R.: Benefiting from the Variables that Variable Selection Discards. Journal of Machine Learning Research 3, 1245–1264 (2003)
Jia, L., Xu, Y.: Guan Xing Bing De Zhen Duan Yu Zhi Liao. Jun Shi Yi Xue Ke Xue Chu Ban She (2001)
Bay, S.D.: Multivariate Discretization of Continuous Variables for Set Mining. In: Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 315–319 (2000)
Bay, S.D.: Multivariate Discretization for Set Mining. Knowledge and Information Systems 3(4), 491–512 (2001)
Gu, W.Q.: Xin Xue Guan Ji Bing Jian Bie Zhen Duan Xue. Xue Yuan Chu Ban She (2006)
Kira, K., Rendell, L.: A Practical Approach to Feature Selection. In: International Conference on Machine Learning, pp. 249–256. Morgan Kaufmann, Aberdeen (1992a)
Kira, K., Rendell, L.: The Feature Selection Problem: Traditional Methods and New Algorithm. In: AAAI 1992, San Jose, CA (1992b)
Mitchell, T.M.: Machine Learning. McGraw-Hill Companies, Inc., New York (1997)
Zhu, X.L.: Fundamentals of Applied Information Theory. Tsinghua University Press (2000)
Fayyad, U.M., Irani, K.B.: Multi-interval Discretization of Continuous-valued Attributes for Classification Learning. In: Thirteenth International Joint Conference on Artificial Intelligence, pp. 1022–1027 (1993)
Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases. Irvine, CA: University of California, Department of Information and Computer Science (1998)
Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1(1), 81–106 (1986)
Langley, P., Iba, W., Thompsom, K.: An Analysis of Bayesian Classifiers. In: Tenth National Conference on Artificial Intelligence, pp. 223–228. AAAI Press and MIT Press (1992)
Almualim, H., Dietterich, T.G.: Learning with Many Irrelevant Features. In: Ninth National Conference on Artifical Intelligence, pp. 547–552. AAAI Press/The MIT Press, Anaheim, California (1992)
Scholkopf, B., Smola, A., Williamson, R.C., Bartlett, P.L.: New Support Vector Algorithms. Neural Computation 12, 1207–1245 (2000)
Kasabov, N.K.: Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering. The MIT Press, Cambridge (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chao, S., Wong, F. (2009). Unearth the Hidden Supportive Information for an Intelligent Medical Diagnostic System. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-02319-4_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02318-7
Online ISBN: 978-3-642-02319-4
eBook Packages: Computer ScienceComputer Science (R0)