Using Genetic Algorithm and ELM Neural Networks for Feature Extraction and Classification of Type 2-Diabetes Mellitus

References

• Aishwarya, S., and S. Anto. 2014. A medical expert system based on genetic algorithm and extreme learning machine for diabetes disease diagnosis. International Journal of Science, Engineering and Technology Research (IJSETR) 3 (5):75–80.
   Google Scholar
• Akusok, A., K. Bjork, Y. Miche, and A. Lendasse. 2015. High-performance extreme learning machines: a complete toolbox for big data applications. IEEE Open Access 3. 1011–1025.
   Google Scholar
• Alharbi, A., and F. Tchier. 2017. Using a genetic-Fuzzy algorithm as a computer aided diagnosis tool on Saudi Arabian breast cancer database. Mathematical Biosciences 286 (2017):39–48. doi:10.1016/j.mbs.2017.02.002.
   PubMedGoogle Scholar
• Bai, Z., L. L. C. Kasun, and G.-B. Huang. 2015. Generic object recognition with local receptive fields based extreme learning machine. INNS Conference on Big Data, San Francisco, August 8–10. doi:10.1016/j.procs.2015.07.316.
   Google Scholar
• Dalir, M. R. 2012a. Feature selection using binary particle swarm optimization and support vector machines for medical diagnosis. Biomedizinische Technik/Biomedical Engineering 57 (5):395–402.
   PubMed Web of Science ®Google Scholar
• Dalir, M. R. 2012b. A hybrid automatic system for the diagnosis of lung cancer based on genetic algorithm and fuzzy extreme learning machines images. Journal of Medical Systems 36,2,1001–1005. Springer US
   PubMed Web of Science ®Google Scholar
• Dalir, M. R. 2012c. Automated diagnosis of Alzheimer disease using the scale-invariant feature transforms in magnetic resonance images. Journal of Medical Systems 36 (2):995–1000. Springer US doi:10.1007/s10916-011-9738-6.
   PubMed Web of Science ®Google Scholar
• Dalir, M. R. 2015. Combining extreme learning machines using support vector machines for breast tissue classification. Computer Methods in Biomechanics and Biomedical Engineering 18 (2):185–91. doi:10.1080/10255842.2013.789100.
   PubMed Web of Science ®Google Scholar
• Destounis, S. V., P. DiNitto, W. Logan-Young, E. Bonaccio, M. L. Zuley, and K. M. Willison. 2004. Can computer-aided detection with double reading of screening mammograms help decrease the false-negative rate? Initial experience. Radiology 232:578–84. doi:10.1148/radiol.2322030034.
   PubMed Web of Science ®Google Scholar
• Duda, R. O., P. E. Hart, and D. G. Stork. 2001. Pattern classification. 2nd ed. New York: Wiley.
   Google Scholar
• Fayssal, B., and M. A. Chikh. 2013. Design of fuzzy classifer for diabetes disease using modifed artifcial Bee colony algorithm. Computer Methods and Programs in Biomedicine 1:92–103.
   Google Scholar
• Foroutan, I., and J. Sklansky. 1987. Feature selection for automatic classification of non Gaussian data. IEEE Transactions on Systems, Man and Cybernetics 17:187–98. doi:10.1109/TSMC.1987.4309029.
   Web of Science ®Google Scholar
• Goldberg, D. 1989. Genetic algorithms in search, optimization, and machine learning. New York: Addison-Wesley.
   Google Scholar
• Habibi, S., M. Ahmadi, and S. Alizadeh. 2015. Type 2 diabetes mellitus screening and risk factors using decision tree: Results of data mining. Global Journal of Health Science 7(5):304–10. Sep. doi: 10.5539/gjhs.v7n5p304.
   PubMedGoogle Scholar
• Huang, C.-L., C. J. Wang. 2006. A GA-Based feature selection and parameters optimization for support vector machines. Expert Systems with Applications 31:231–40, Elsevier. doi:10.1016/j.eswa.2005.09.024.
   Web of Science ®Google Scholar
• Huang, G., G.-B. Huang, S. Song, and K. You. 2015. Trends in extreme learning machines: a review. Neural Networks 61 (1):32–48. doi:10.1016/j.neunet.2014.10.001.
   PubMedGoogle Scholar
• Huang, G. B., H. Zhou, X. Ding, and R. Zhang. 2012. Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 42 (2):513–29. doi:10.1109/TSMCB.2011.2168604.
   PubMed Web of Science ®Google Scholar
• Huang, G. B., Q.-Y. Zhu, and C.-K. Siew. 2004. Extreme learning machine: A new learning scheme of feedforward neural networks. International Joint Conference on Neural Networks (IJCNN’2004), Budapest, Hungary, July 25–29.
   Google Scholar
• Huang, G. B., Q.-Y. Zhu, and C.-K. Siew. 2006. Extreme learning machine: theory and applications. Neurocomputing 70:489–501. doi:10.1016/j.neucom.2005.12.126.
   Web of Science ®Google Scholar
• Huang, G. B., X. Ding, and H. Zhou. 2010. Optimization method based extreme learning machine for classification. Neurocomputing 74:155–63. doi:10.1016/j.neucom.2010.02.019.
   Web of Science ®Google Scholar
• Koza, J. R. 1992. Genetic programming. Cambridge, MA: MIT Press.
   Google Scholar
• Langley, P. 1995. Elements of machine learning. Palo Alto, CA: Morgan Kaufmann.
   Google Scholar
• Liu, H., and R. Setiono (1996a). Feature selection and classification - A probabilistic wrapper approach. Proceedings of the Ninth International Conference on Industrial and Engineering Applications of AI and ES, June 04 - 07, Fukuoka, Japan, pp. 419–424.
   Google Scholar
• Mathworks, Matlab tool box guide. Accessed Jan, 2015. http://www.mathworks.com/products/global-optimization/features.html#genetic-algorithm-solver.
   Google Scholar
• Michalewicz, Z. 1996. Genetic algorithms + data structures= evolution programs. 3rd ed. Berlin: Springer-Verlag.
   Google Scholar
• Mitchell, M. 1996. An introduction to genetic algorithms. Cambridge, MA: MIT Press.
   Google Scholar
• Narendra, P., and K. Fukunaga. 1977. A branch and Bound algorithm for feature subset selection. IEEE Transactions on Computers 26:917–22. doi:10.1109/TC.1977.1674939.
   Web of Science ®Google Scholar
• National Diabetes Services Schema (NDSS). 2017. Accessed 2016. https://www.ndss.com.au/gestational-diabetes.
   Google Scholar
• Parekh, R., J. Yang, and V. Honavar. 1996. Constructive neural network learning algorithms for multi-category real-valued pattern classification. Tech. rept. TR 96–14, Department of Computer Science, Iowa State University.
   Google Scholar
• Punch, W., E. Goodman, M. Pei, L. Chia-Shun, P. Hovland, and R. Enbody. 1993. Further research on feature selection and classification using genetic algorithms. In Proceedings of international conference on genetic algorithms, 557–64. San Francisco, CA: Springer.
   Google Scholar
• Ren, Y., and G. Bai. 2010. Determination of optimal SVM parameters by using genetic algorithm/particle swarm optimization. Journal of Computers 5 (8):160–116.
   Google Scholar
• Ripley, B. 1996. Pattern recognition and neural networks. New York: Cambridge University Press.
   Google Scholar
• Siedlecki, W., and J. Sklansky. 1989. A note on genetic algorithms for large-scale feature selection. IEEE Transactions on Computers 10:335–47.
   Google Scholar
• Temurtas, H., N. Yumusak, and F. Temurtas. 2009. A comparative study on diabetes disease diagnosis using neural networks. Expert Systems with Applications 36:8610–15. Elsevier. doi:10.1016/j.eswa.2008.10.032.
   Web of Science ®Google Scholar
• Turney, P. 1995. Cost-sensitive classification: empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of Artificial Intelligence Research 2:369–409. doi:10.1613/jair.120.
   Google Scholar
• Vafaie, H., and K. De Jong. 1993. Robust feature selection algorithms. Proceedings of the IEEE international conference on tools with artificial intelligence, Herndom, VA, pp. 356–63.
   Google Scholar
• Wahabi, H., A. Fayed, S. Esmaeil, H. Mamdouh, and R. Kotb. 2017 March. Prevalence and complications of pre-gestational and gestational diabetes in Saudi women analysis from Riyadh Mother and Baby Cohort Study (RAHMA). BioMed Research International 11(3):1–9.
   Google Scholar
• Zhu, J., Q. Xie, and K. Zheng. 2015. An improved early detection method of type 2- diabetes mellitus using multiple classifier system. Information Science 292:1–14. doi:10.1016/j.ins.2014.08.056.
   Web of Science ®Google Scholar