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.
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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.
Werner, S. C., Classification of thyroid diseases. Report of the committee on nomenclature. American Thyroid Association. J. Clin. Endocrinol. Metab. 29:860–862, 1969.
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.
Monaco, F., Classification of thyroid diseases: suggestions for a revision. J. Clin. Endocrinol. Metab. 88:1428–1432, 2003.
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.
Feld, S., et al., AACE Clinical guidelines for the diagnosis and management of thyroid nodules. Endocr. Pract. 2(1):78–84, 1996.
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.
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.
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.
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.
Ergun, U., et al., Classification of MCA stenosis in diabetes by MLP and RBF neural network. J. Med. Syst. 28(5):475–487, 2004.
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.
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.
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.
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.
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.
Zhang, G. P., and Berardi, V. L., An investigation of neural networks in thyroid function diagnosis. Health Care Manage. Sci. 1:29–37, 1998.
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.
Zhang, H., and Lin, F. C., Medical diagnosis by virtual physician. 12th IEEE Symposium on Computer-Based Medical Systems, 1999.
Krose, B., and Smaget, P. V. D., An introduction to neural networks. Amsterdam, The University of Amsterdam Press, 1996.
Haykin, S., Neural networks: a comprehensive foundation. New York, Macmillan, 1994.
SAS Institute Inc., ftp://ftp.sas.com/pub/neural/FAQ2.html, 2002.
Duda, R. O., Hart, P. E., and Stork, D. G., Pattern classification. New York, Wiley, 2000.
Bernand, E., Optimization training neural nets. IEEE Trans. Neural Netw. 3:2989–993, 1992.
Hagan, M. T., and Menhaj, M. B., Training feed forward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 5(6):989–993, 1994.
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.
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.
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.
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.
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.
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.
Acknowledgment
The authors would like to express their respect to the memory of late Dr. Mustafa Kocak who had given the inspiration of this study and thank to Cukurova University Research Hospital personnel for providing medical data for this study.
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Erol, R., Oğulata, S.N., Şahin, C. et al. A Radial Basis Function Neural Network (RBFNN) Approach for Structural Classification of Thyroid Diseases. J Med Syst 32, 215–220 (2008). https://doi.org/10.1007/s10916-007-9125-5
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DOI: https://doi.org/10.1007/s10916-007-9125-5