Halife Kodaz
ABSTRACT
The use of artificial intelligence methods in medical diagnosis is increasing gradually. The effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Artificial Immune Systems (AIS) is a new but effective branch of artificial intelligence. This study aims at diagnosing thyroid disease with Artificial Immune Recognition System (AIRS). Thyroid disease diagnosis is an important classification problem. The thyroid data employed in this study is available from UCI Repository site. This data set is a very commonly used data set in the literature relating the use of classification systems for thyroid disease diagnosis and it was used in this study to compare the classification performance of AIRS with regard to other studies. We obtained a classification accuracy of 94.82%, which is one of the highest accuracies reached so far. This result ensured that AIRS would be helpful in diagnosing thyroid function based on laboratory tests, and would open the way to various ill diagnoses support by using the recent clinical examination data, and we are actually in progress.
- De Castro, L. N. and Von Zuben, F. J. 1999. Artificial Immune System: part I-basic theory and applications. Technical Report, TR-DCA 01/99.Google Scholar
- De Castro, L. N. and Timmis, J. 2002. Artificial Immune Systems: A New Computational Intelligence Approach, Springer, Berlin. Google ScholarDigital Library
- Latifoğlu, F., Kodaz, H., Kara, S., and Güneş, S. 2007. Medical application of Artificial Immune Recognition System (AIRS): Diagnosis of atherosclerosis from carotid artery Doppler signals, Computers in Biology and Medicine, vol. 37, 1092--1099. Google ScholarDigital Library
- Sahan, S., Polat, K., Kodaz, H., and Güneş, S. 2005. The Medical Applications of Attribute Weighted Artificial Immune System (AWAIS): Diagnosis of Heart and Diabetes Diseases, Lectures Notes in Computer Science, Springer, Berlin, vol. 3627, 456--468. Google ScholarDigital Library
- Forrest, S., Hoymeyr, S. A., and Somayaji, A. 1997. Computer Immunology, Communications of the ACM, vol. 40 (10), 88--96. Google ScholarDigital Library
- Hofmeyr, S. A., Forrest, S. 1999. Immunity by design: An artificial immune system, in Proceedings of the genetic and evolutionary computation conference (GECCO), 1289--1296.Google Scholar
- Timmis, J., Neal, M., and Hunt, J. 2000. An artificial immune system for data analysis, Biosystems, vol. 55 (1/3), 143--150.Google ScholarCross Ref
- Timmis, J., Neal, M., and Hunt, J. 1999. Data analysis with artificial immune systems, cluster analysis and Kohonen Networks: Some comparisons, in Proceedings of the International Conference on Systems, Man, and Cybernetics, 922--927.Google Scholar
- Carter, J. 2000. The immune system as a model for pattern recognition and classification, J American Medical Informatics Association (JAMIA), vol. 7(1), 28--41.Google ScholarCross Ref
- Watkins, A. 2001. AIRS: A resource limited artificial immune classifier, M.S. thesis, Mississippi State University, USA.Google Scholar
- Hettich, S., Blake, C. L., and Mertz, C. J. 2007. UCI Repository of Machine Learning Databases, University of California at Irvine, Department of Computer Science, http://www.ics.uci.edu/~mlearn/databases/thyroid-disease/new-thyroid.data, last accessed 28 August 2009.Google Scholar
- Cheong, T. S. and Yoon, C. H. 1999. A Memory Based Classifier Using The Recursive Partition Averaging, IEEE Tencon, 1038--1041.Google Scholar
- Kim, H. C. and Ghahramani, Z. 2006. Bayesian Gaussian Process Classification with the EM-EP Algorithm, IEEE Transactions On Pattern Analysis and Machine Intelligence, vol. 28(12), 1948--1959. Google ScholarDigital Library
- Sun, Y. 2007. Cost-Sensitive Boosting for Classification of unbalanced Data, P.H.D. thesis, Waterloo University, Canada. Google ScholarDigital Library
- Myles, A. J. and Brown, S. D. 2004. Decision pathway modeling, Journal of Chemometrics, vol. 18, 286--293.Google ScholarCross Ref
- Yip, S. P. and Webb, G. I. 1994. Empirical Function Attribute Construction in Classification Learning, Joint Conference on Artificial Intelligence (AI'94), 29--36.Google Scholar
- Albayrak, S. and Amasyali, F. 2003. Fuzzy C-Means Clustering on Medical Diagnostic Systems, International XII. Turkish Symposium on Artificial Intelligence and Neural Networks-TAINN 2003, http://www.ce.yildiz.edu.tr/mygetfile.php?id=269, last accessed 28 August 2009.Google Scholar
- Hassan, R., Nath, B., and Kirley, M. 2006. A Data Clustering Algorithm Based On Single Hidden Markov Model, Proceedings of the International Multiconference on Computer Science and Information Technology, 57--66.Google Scholar
- Pechenizkiy, M., Tsymbal, A., Puuronen, S., and Patterson, D. W. 2007. Feature Extraction for Dynamic Integration of Classifiers, Fundamenta Informaticae, 77(3), 243--275. Google ScholarDigital Library
- Özyilmaz, L. and Yildirim, T. 2002. Diagnosis of Thyroid Disease Using Artificial Neural Network Methods, Proceedings of the 9th International Conference on Neural Information Processing (ICONIP'02), vol. 4, 2033--2036.Google Scholar
- http://www.endocrineweb.com/thyroid.html, last accessed 28 August 2009.Google Scholar
- Zhang, G. and Berardi, L. V. 1998. An investigation of neural networks in thyroid function diagnosis. Health Care Management Science, 29--37.Google Scholar
- Coomans, D., Broeckaert, I., Jonckheer, M., and Massart, D. L. 1983. Comparison of multivariate discrimination techniques for clinical data---application to the thyroid functional state. Methods Information in Medicine, vol. 22, 93--101.Google ScholarCross Ref
- Nossal, G. J. V. 1993. Life, Death and the Immune System, Scientific American, vol. 269 (3), 21--30.Google ScholarCross Ref
- Abbas, A. K., Lichtman, A. H., and Pober, J. S. 2000. Cellular and Molecular Immunology, fourth ed., W. B. Saunders Company.Google Scholar
- Dasgupta, D. 1999. Artificial Immune Systems and Their Applications, Springer, Berlin. Google ScholarDigital Library
- Sahan, S., Kodaz, H., Güneş, S., and Polat, K. 2004. A New Classifier Based on Attribute Weighted Immune System (AWAIS), Lectures Notes in Computer Science, Springer, Berlin, vol. 3280, 11--20.Google Scholar
- Kohavi, R. 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection, In C. Mellish (Ed.), Proceedings of 14th International Joint Conference on Artificial Intelligence IJCAI-95, San Francisco, Morgan Kaufmann, 1137--1145. Google ScholarDigital Library
Index Terms
- Thyroid disease diagnosis using Artificial Immune Recognition System (AIRS)
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