Journal of Medical Systems

, Volume 32, Issue 3, pp 215–220 | Cite as

A Radial Basis Function Neural Network (RBFNN) Approach for Structural Classification of Thyroid Diseases

  • Rızvan Erol
  • Seyfettin Noyan Oğulata
  • Cenk Şahin
  • Z. Nazan Alparslan
Original Paper

Abstract

The thyroid is a gland that controls key functions of body. Diseases of the thyroid gland can adversely affect nearly every organ in human body. The correct diagnosis of a patient’s thyroid disease clarifies the choice of drug treatment and also allows an accurate assessment of prognosis in many cases. This study investigates Multilayer Perceptron Neural Network (MLPNN) and Radial Basis Function Neural Network (RBFNN) for structural classification of thyroid diseases. A data set for 487 patients having thyroid disease is used to build, train and test the corresponding neural networks. The structural classification of this data set was performed by two expert physicians before the input variables and results were fed into the neural networks. Experimental results show that the predictions of both neural network models are very satisfying for learning data sets. Regarding the evaluation data, the trained RBFNN model outperforms the corresponding MLPNN model. This study demonstrates the strong utility of an artificial neural network model for structural classification of thyroid diseases.

Keywords

Radial Basis Function Neural Network (RBFNN) Multilayer Perceptron Neural Network (MLPNN) Levenberg-Marquardt Structural classification Thyroid 

References

  1. 1.
    Werner, S. C., and Ingbar, S. H., Diseases of the thyroid. In: Werner, S. C., Ingbar S. H., et al., (Eds.), The thyroid: A fundamental and clinical text. 4th Ed. New York: Harper and Row, 1978, pp. 389–393.Google Scholar
  2. 2.
    Werner, S. C., Classification of thyroid diseases. Report of the committee on nomenclature. American Thyroid Association. J. Clin. Endocrinol. Metab. 29:860–862, 1969.CrossRefGoogle Scholar
  3. 3.
    Braverman, L.E., and Utiger, R.D. (Eds.), The thyroid: a fundamental and clinical text, 8th Ed. Philadelphia, Lippincot Williams & Wilkins, 2000, pp. 515–719.Google Scholar
  4. 4.
    Monaco, F., Classification of thyroid diseases: suggestions for a revision. J. Clin. Endocrinol. Metab. 88:1428–1432, 2003.CrossRefGoogle Scholar
  5. 5.
    Grünwald, F.B., Thyroid disease. In: Ell, P.J., and Gambhir, S.S., (Eds.), Nuclear medicine in clinical diagnosis and treatment. New York: Churchill Livingstone, pp. 383–392, 2004.Google Scholar
  6. 6.
    Feld, S., et al., AACE Clinical guidelines for the diagnosis and management of thyroid nodules. Endocr. Pract. 2(1):78–84, 1996.Google Scholar
  7. 7.
    Selvi, S. T., Arumugam, S., and Ganesan, L., BIONET: An artificial neural network model for diagnosis of diseases. Pattern Recogn. Lett. 21:721–740, 2001.CrossRefGoogle Scholar
  8. 8.
    Veezhinathan, M., and Ramakrishnan, S., Detection of obstructive respiratory abnormality using flow-volume spirometry and radial basis function neural networks. J. Med Syst. 31:461–465, 2007.CrossRefGoogle Scholar
  9. 9.
    Sahin, C., Ogulata, S. N., Aslan, K., and Bozdemir, H., Application of neural networks in classification of epilepsy using EEG signals. Lect. Notes Comput. Sci. 4729:499–508, 2007.CrossRefGoogle Scholar
  10. 10.
    Srinivasan, V., Eswaran, C., and Sriraam, N., Artificial neural network based epileptic detection using time-domain and frequency-domain features. J. Med. Syst. 29(6):647–660, 2005.CrossRefGoogle Scholar
  11. 11.
    Ergun, U., et al., Classification of MCA stenosis in diabetes by MLP and RBF neural network. J. Med. Syst. 28(5):475–487, 2004.CrossRefGoogle Scholar
  12. 12.
    Yildirim, H., et al., Classification of the frequency of carotid artery stenosis with MLP and RBF neural networks in patients with coroner artery disease. J. Med. Syst. 28(6):591–301, 2004.CrossRefGoogle Scholar
  13. 13.
    Gogou, G., Maglaveras, N., Ambrosiadou, B. V., Goulis, D., and Pappas, C., A neural network approach in diabetes management by insulin administration. J. Med. Syst. 25:2119–131, 2001.CrossRefGoogle Scholar
  14. 14.
    Walzak, S., and Nowack, W. J., An artificial neural network to diagnosing epilepsy using lateralized burst of theta EEGs. J. Med. Syst. 25:19–20, 2001.CrossRefGoogle Scholar
  15. 15.
    Kwak, N. K., and Lee, C., A neural network application to classification of health status of HIV/AIDS patients. J. Med. Syst. 21(2):87–97, 1997.CrossRefGoogle Scholar
  16. 16.
    Sharpe, P. K., Solberg, H. E., Rootwelt, K., and Yearworth, M., Artificial neural networks in diagnosis of thyroid function from vitro laboratory tests. Clin. Chem. 39:2248–2253, 1993.Google Scholar
  17. 17.
    Zhang, G. P., and Berardi, V. L., An investigation of neural networks in thyroid function diagnosis. Health Care Manage. Sci. 1:29–37, 1998.CrossRefGoogle Scholar
  18. 18.
    Ping, W. L., Phuan, A. T. L., and Jian, X., Hierarchical fast learning artificial neural network: progressive learning in high dimensional spaces. International Report, 2004.Google Scholar
  19. 19.
    Zhang, H., and Lin, F. C., Medical diagnosis by virtual physician. 12th IEEE Symposium on Computer-Based Medical Systems, 1999.Google Scholar
  20. 20.
    Krose, B., and Smaget, P. V. D., An introduction to neural networks. Amsterdam, The University of Amsterdam Press, 1996.Google Scholar
  21. 21.
    Haykin, S., Neural networks: a comprehensive foundation. New York, Macmillan, 1994.MATHGoogle Scholar
  22. 22.
    SAS Institute Inc., ftp://ftp.sas.com/pub/neural/FAQ2.html, 2002.
  23. 23.
    Duda, R. O., Hart, P. E., and Stork, D. G., Pattern classification. New York, Wiley, 2000.Google Scholar
  24. 24.
    Bernand, E., Optimization training neural nets. IEEE Trans. Neural Netw. 3:2989–993, 1992.Google Scholar
  25. 25.
    Hagan, M. T., and Menhaj, M. B., Training feed forward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 5(6):989–993, 1994.CrossRefGoogle Scholar
  26. 26.
    Fontenla-Romero, O., Erdogmus, D., Principe, J. C., Alonso-Betanzos, A., and Castillo, E., Accelerating the converge speed of neural networks learning methods using least squares. European Symposium on Artificial Neural Networks, 2003, pp. 255–260.Google Scholar
  27. 27.
    Wilamowki, B. M., Iqlikci, S., Kaynak, O., and Onder, E. M., An algorithm for fast converge in training neural networks. IEEE Proceedings of International Joint Conference on Neural Networks, pp. 1778–1782, 2005.Google Scholar
  28. 28.
    Lera, G., and Pinzolas, M., A quasi-local Levenberg–Marquardt algorithm for neural network training. IEEE World Congress on Computational Intelligence 3:2242–2246, 1998.CrossRefGoogle Scholar
  29. 29.
    Manolis, I. A. L., and Antonis, A. A., Is Levenberg–Marquardt the most efficient optimization algorithm for implementing bundle adjustment. IEEE Proceedings of International Conference on Computer Vision 2:1526–1531, 2005.Google Scholar
  30. 30.
    Lee, C, Chung, P, Tsai, J, and Chang, C, Robust radial basis function neural networks. IEEE Transactions on Systems, Man, and Cybernetics—B: Cybernetics 29:674–685, 1999.Google Scholar
  31. 31.
    Ergun, U., Serhatlioglu, S., Hardalac, F., and Guler, I., Classification of carotid artery stenosis of the patients with diabetes by neural network and logistic regression. Comput. Biol. Med. 34:389–405, 2004.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Rızvan Erol
    • 1
  • Seyfettin Noyan Oğulata
    • 1
  • Cenk Şahin
    • 1
  • Z. Nazan Alparslan
    • 2
  1. 1.Department of Industrial Engineering, Faculty of Engineering and ArchitectureCukurova UniversityAdanaTurkey
  2. 2.Department of Biostatistics, Faculty of MedicineCukurova UniversityAdanaTurkey

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