A Computer Aided Diagnosis System for Thyroid Disease Using Extreme Learning Machine
- 493 Downloads
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.
KeywordsThyroid disease diagnosis Extreme learning machine (ELM) Principle component analysis (PCA)
- 1.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.Google Scholar
- 2.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.Google Scholar
- 3.Pasi, L., Similarity classifier applied to medical data sets, in international conference on soft computing. Helsinki, Finland & Gulf of Finland & Tallinn, Estonia, 2004.Google Scholar
- 8.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.
- 10.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.Google Scholar
- 12.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.Google Scholar
- 15.Helmy, T., and Rasheed, Z., Multi-category bioinformatics dataset classification using extreme learning machine. in Evolutionary Computation, 2009. CEC '09. IEEE Congress on. 2009.Google Scholar
- 16.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.Google Scholar
- 21.Pearson, K., On lines and planes of closest fit to systems of points in space. Philos. Mag. 2(6):559–572, 1901.Google Scholar
- 27.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.Google Scholar
- 28.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.
- 29.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/.