Abstract
The purpose of this paper is to examine the unsupervised clustering methods on medical data. Neural networks and statistical methods can be used to develop an accurate automatic diagnostic system. Self-Organizing Feature map as a Neural Network model and K-means as a statistical model are tested to predict a well defined class. To test the diagnostic system, thyroid gland data is used for the application. As a result of clustering algorithms, patients are classified normal, hyperthyroid function and hypothyroid function.
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References
Coomans, D., Broeckaert, I., Jonckheer, M., Massart D.L.: Comparison of Multivariate Discrimination Techniques for Clinical Data-Application to the Thyroid Functional State. Methods of Information in Medicine, Vol.22, (1983) 93–101
Ozyilmaz L., Yildirim T., Diagnosis of Thyroid Disease using Artificial Neural Network Methods, Proceedings of the 9’th International Conference on Neural Information Processing (ICONIP’02) (2002)
Tou, J.T., Gonzalez R.C., Pattern Recognition Principles, Addison-Wesley Publishing Company (1974)
R.J. Schalkoff, Pattern Recognition: Statistical, Structural and Neural Approaches, John Wiley & Sons, Inc. (1992)
Haykin S., Neural Networks: A Comprehensive Foundation, Macmillan College Publishing Company, New York (1994)
N.K. Bose, P. Liang, Neural Network Fundamentals with Graphs, Algorithms and Applications, Mc Graw Hill (1996)
Carpenter A.G., Markuzon N., ARTMAP-IC and medical diagnosis: Instance counting and inconsistent cases, Elsevier Neural Networks 11, 323–336, (1998)
Deng D., Kasabov N., On-line Pattern Analysis by Evolving Self-Organizing Maps, (2000)
www.endocrineweb.com/thyroid.html, 2002
www.ics.uci.edu/pub/ml-repos/machine-learning-database/, 2001
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© 2003 Springer-Verlag Berlin Heidelberg
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Albayrak, S. (2003). Unsupervised Clustering Methods for Medical Data: An Application to Thyroid Gland Data. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_83
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DOI: https://doi.org/10.1007/3-540-44989-2_83
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