Abstract
In this paper, we present an effective and efficient computer aided diagnosis (CAD) system based on principle component analysis (PCA) and extreme learning machine (ELM) to assist the task of thyroid disease diagnosis. The CAD system is comprised of three stages. Focusing on dimension reduction, the first stage applies PCA to construct the most discriminative new feature set. After then, the system switches to the second stage whose target is model construction. ELM classifier is explored to train an optimal predictive model whose parameters are optimized. As we known, the number of hidden neurons has an important role in the performance of ELM, so we propose an experimental method to hunt for the optimal value. Finally, the obtained optimal ELM model proceeds to perform the thyroid disease diagnosis tasks using the most discriminative new feature set and the optimal parameters. The effectiveness of the resultant CAD system (PCA-ELM) has been rigorously estimated on a thyroid disease dataset which is taken from UCI machine learning repository. We compare it with other related methods in terms of their classification accuracy. Experimental results demonstrate that PCA-ELM outperforms other ones reported so far by 10-fold cross-validation method, with the mean accuracy of 97.73% and with the maximum accuracy of 98.1%. Besides, PCA-ELM performs much faster than support vector machines (SVM) based CAD system. Consequently, the proposed method PCA-ELM can be considered as a new powerful tools for diagnosing thyroid disease with excellent performance and less time.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ozyilmaz, L., and Yildirim T., Diagnosis of thyroid disease using artificial neural network methods. In Proceedings of ICONIP’02 nineth international conference on neural information processing, Orchid Country Club, Singapore, pp. 2033–2036, 2002.
Serpen, G., Jiang, H., and Allred, L., Performance analysis of probabilistic potential function neural network classifier. In Proceedings of artificial neural networks in engineering conference, St. Louis, MO, Vol. 7, pp. 471–476, 1997.
Pasi, L., Similarity classifier applied to medical data sets, in international conference on soft computing. Helsinki, Finland & Gulf of Finland & Tallinn, Estonia, 2004.
Polat, K., Sahan, S., and Gunes, S., A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis. Expert Syst. Appl. 32(4):1141–1147, 2007.
Keles, A., and Keles, A., ESTDD: expert system for thyroid diseases diagnosis. Expert Syst. Appl. 34(1):242–246, 2008.
Temurtas, F., A comparative study on thyroid disease diagnosis using neural networks. Expert Syst. Appl. 36(1):944–949, 2009.
Dogantekin, E., Dogantekin, A., and Avci, D., An expert system based on generalized discriminant analysis and wavelet support vector machine for diagnosis of thyroid diseases. Expert Syst. Appl. 38(1):146–150, 2011.
Chen, H. L, Yang, B., Wang, G., Liu, J., Chen, Y. D., and Liu., D. Y., “A three-stage expert system based on support vector machines for thyroid disease diagnosis.” J. Med. Syst.: http://dx.doi.org/10.1007/s10916-011-9655-8, 2011.
Hsu, C. W., and Lin, C. J., A comparison of methods for multi-class support vector machines. Neural Networks, IEEE Transactions on. 13(2):415–425, 2002.
Huang, G. B., Zhu, Q. Y., and Siew, C. K., Extreme learning machine: a new learning scheme of feed forward neural networks. IEEE Int. Jt. Conf. Neural Netw. 2:985–990, 2004.
Chen, F. L., and Ou, T. Y., Sales forecasting system based on gray extreme learning machine with taguchi method in retail industry. Expert Syst. Appl. 38(3):1336–1345, 2011.
Liu, N., Lin, Z., Koh, Z., Huang, G. B, Ser, W., Ong, M. E. H., Patient outcome prediction with heart rate variability and vital signs. J. Signal Proc. Syst. 1–14, 2010.
Han, F., et al., The forecast of the postoperative survival time of patients suffered from non-small cell lung cancer based on PCA and extreme learning machine. Int. J. Neural Syst. 16(1):39–46, 2006.
Zhang, R., et al., Multicategory classification using an extreme learning machine for microarray gene expression cancer diagnosis. IEEE/ACM Trans. Comput. Biol. Bioinforma. 4(3):485–494, 2007.
Helmy, T., and Rasheed, Z., Multi-category bioinformatics dataset classification using extreme learning machine. in Evolutionary Computation, 2009. CEC '09. IEEE Congress on. 2009.
Gomathi, M., and Thangaraj, P., A computer aided diagnosis system for lung cancer detection using machine learning technique. Eur. J. Sci. Res. 51(2):260–275, 2011.
Chen, H. L., Liu, D. Y., Yang, B., Liu, J., and Wang, G., A new hybrid method based on local fisher discriminant analysis and support vector machines for hepatitis disease diagnosis. Expert Syst. Appl. 38(9):11796–11803, 2011.
Chen, H. L., Yang, B., Liu, J., and Liu, D. Y., A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst. Appl. 38(7):9014–9022, 2011.
Polat, K., and Gunes, S., Computer aided medical diagnosis system based on principal component analysis and artificial immune recognition system classifier algorithm. Expert Syst. Appl. 34(1):773–779, 2008.
Polat, K., and Gunes, S., An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digit. Signal Proc. 17(4):702–710, 2007.
Pearson, K., On lines and planes of closest fit to systems of points in space. Philos. Mag. 2(6):559–572, 1901.
Huang, G.-B., Zhu, Q. Y., and Siew, C.-K., Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501, 2006.
Huang, G. B., and Babri, H. A., Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions. Neural Netw., IEEE Trans. on. 9(1):224–229, 1998.
Huang, G. B., Learning capability and storage capacity of two-hidden-layer feedforward networks. Neural Netw., IEEE Trans. on. 14(2):274–281, 2003.
Huang, G. B., Chen, L., and Siew, C. K., Universal approximation using incremental constructive feedforward networks with random hidden nodes. Neural Netw., IEEE Trans. on. 17(4):879–892, 2006.
Salzberg, S. L., On comparing classifiers: pitfalls to avoid and a recommended approach. Data mining. Knowl. Discov. 1(3):317–328, 1997.
Ron, K., A study of cross-validation and bootstrap for accuracy estimation and model selection, in Proceedings of the 14th international joint conference on Artificial intelligence—Vol2, 1995.
Chang, C. C., and Lin, C. J., LIBSVM: a library for support vector machines. 2001, Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm.
Hsu, C. W., Chang, C. C., and Lin, C. J., A practical guide to support vector classification. Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 2003. available at http://www.csie.ntu.edu.tw/cjlin/libsvm/.
Acknowledgements
This research is supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61133011, 61170092, 60973088, 60873149.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Li, LN., Ouyang, JH., Chen, HL. et al. A Computer Aided Diagnosis System for Thyroid Disease Using Extreme Learning Machine. J Med Syst 36, 3327–3337 (2012). https://doi.org/10.1007/s10916-012-9825-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10916-012-9825-3