Breast Cancer Prognosis Using Learning Vector Quantization Neural Network Technique

  • Authors

    • W. Abdul Hameed
    • Raja Das
    • Jitendra Jaiswal
    2018-10-02
    https://doi.org/10.14419/ijet.v7i4.10.26789
  • Neural networks, Learning Vector Quantization, Breast cancer Prognosis, recurrence / non-recurrence, Classification.

  • A suitable treatment coming after surgery is very much motivated by prognosis - the speculated outcome of the disease. Now-a-days improving prognostic prediction is a challenging task to the doctors. This paper presents prognosis for the breast cancer issues by applying Neural Network Architecture with the dataset for Wisconsin Prognostic Breast Cancer. The accuracy is evaluated by adopting algorithm for Kohonen’s first issue of Learning Vector Quantization to predict the recurrence of the disease within 2 years or beyond and also within 5 years or beyond.

     

     

  • References

    1. [1] W. H. Wolberg, W. N. Street, and O. L. Mangasarian (1994), Machine Learning Techniques to diagnose Breast Cancer from Image-processed Nuclear Features of Fine Needle Aspirates, Cancer Letters, vol. 77, pp. 163-171.

      [2] W. N. Street (2000), Xcyt: A system for remote cytological diagnosis and prognosis of breast cancer, soft-computing Techniques in Breast Cancer Prognosis and Diagnosis, World Scientific Publications.

      [3] R. M. Ripley (1998), Neural network for Breast cancer Prognosis, Ph.D., Thesis, Department of Engineering Science, University of Oxford.

      [4] H. B. Burke, and P. H. Goodman (1997), Artificial neural network improve the accuracy of cancer survival prediction, Cancer Journal, vol. 99, pp. 857-862.

      [5] P. L. Choong, and C. J. S. deSilva (1996), Entropy maximization networks, An application to breast cancer prognosis, IEEE Transaction Neural Network, vol. 7, pp. 568-577.

      [6] P. L. Choong, and C. J. S. deSilva (1998), Maximum entropy estimation vs. multivariate logistic regression: Which should be used for the analysis of small binary outcome data sets ?, Proc. 20th Ann. Int. Conf. IEEE Eng. Med. Biol. Soc., vol. 3, pp. 1602-1605.

      [7] H. Sekar, M. Odetayo, D. Petrovic, R. N. Naguib, C. Bartolic, L. Alasio, M. S. Lakshmi and G. V. Sherbet (2000), A fuzzy measurement based assessment of breast cancer prognostic markers, Proc. Of 2000 IEEE EMBS Int. conf. on Information Tech. Applications in Biomedicine, pp174-178.

      [8] O. L. Mangasarian, W. N. Street, and W. H. Wolberg (1995), Breast cancer diagnosis and prognosis via linear programming, Operations Research, vol. 43, no. 4, pp. 570-577.

      [9] W. N. Street (1994), Cancer diagnosis and prognosis via linear programming based machine learning, Ph.D. dissertation, University of Wisconsin.

      [10] http://ftp.ics.uci.edu/pub/machine-learning-databases/breast cancer-Wisconsin/Wisconsin Diagnosis Breast Cancer (WDBC) Dataset and Wisconsin Prognostic Breast Cancer (WPBC) Dataset

      [11] W. H. Wolberg, SW. N. Street, and O. L. Mangasarian (1995), Image analysis and machine learning applied to breast cancer diagnosis and prognosis, Anal Quant cytol , vol. 17, pp. 77-87.

      [12] W. S. McCullogh (1943), W. Pitts, Bull Maths Biophysics, vol. 5, pp. 115-118.

      [13] D. E. Rummelhart, J. L. McLelland (1987), Parallel distributed processing: Explorations in the microstructure of Cognition, Foundation, MIT Press, vol. I.

      [14] J. J. Hopfield (1982), Neural networks and physical systems with emergent collective computational abilities, Proc. Nat. Acad. Sci. pp. 2554-2558.

      [15] T. Kohonen (1982), Self-organized formation of topologically correct feature maps, Biological cybernetics, vol. 43, pp. 59-69.

  • Downloads

  • How to Cite

    Abdul Hameed, W., Das, R., & Jaiswal, J. (2018). Breast Cancer Prognosis Using Learning Vector Quantization Neural Network Technique. International Journal of Engineering & Technology, 7(4.10), 922-924. https://doi.org/10.14419/ijet.v7i4.10.26789