Ensemble Comparative Study for Diagnosis of Breast Cancer Datasets

  • Authors

    • Bibhuprasad Sahu
    • Sujata Dash
    • Sachi Nandan Mohanty
    • Saroj Kumar Rout
    2018-10-07
    https://doi.org/10.14419/ijet.v7i4.15.23007
  • Classification, Neural Network, features selection, PCA, LDA, NB, RF.
  • Every disease is curable if a little amount of human effort is applied for early diagnosis. The death rate in world increases day by day as patient fail to detect it before it becomes chronic. Breast cancer is curable if detection is done at early stage before it spread across all part of body. Now-a-days computer aided diagnosis are automated assistance for the doctors to produce accurate prediction about the stage of disease. This study provided CAD system for diagnosis of breast cancer. This method uses Neural Network (NN) as a classifier model and PCA/LDA for dimension reduction method to attain higher classification rate. Multiple layers of neural network are applied to classify the breast cancer data. This system experiment done on Wisconsin breast cancer dataset (WBCD) from UCI repository. The dataset is divided into 2 parts train and test. With the result of accuracy, sensitivity, specificity, precision and recall the performance can be measured. The results obtained are this study is 97% using ANN and PCA-ANN, which is better than other state-of-art methods. As per the result analysis this system outperformed then the existing system.

     

     

  • References

    1. [1] Prevention Control: Center for Diseases Control and Prevention(2014).URL https://www.cdc.gov/cancer/breast/index.htm.

      [2] U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2008 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control.

      [3] Torre LA, Bray F, Siegel RL, Ferlay J, Lortetâ€Tieulent J, Jemal A. Global cancer statistics, 2012. CA: a cancer journal for clinicians. 2015 Mar;65(2):87-108.

      [4] Mert A, Kılıç N, Bilgili E, Akan A. Breast cancer detection with reduced feature set. Computational and mathematical methods in medicine. 2015;2015.

      [5] Bhattacherjee A, Roy S, Paul S, Roy P, Kausar N, Dey N. Classification approach for breast cancer detection using back propagation neural network: a study. InBiomedical image analysis and mining techniques for improved health outcomes 2016 (pp. 210-221). IGI Global.

      [6] Karaa WB, editor. Biomedical image analysis and mining techniques for improved health outcomes. IGI Global; 2015 Nov 3.

      [7] Azar AT, El-Said SA. Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Computing and Applications. 2014 Apr 1;24(5):1163-77.

      [8] Jhajharia S, Varshney HK, Verma S, Kumar R. A neural network based breast cancer prognosis model with PCA processed features. InAdvances in Computing, Communications and Informatics (ICACCI), 2016 International Conference on 2016 Sep 21 (pp. 1896-1901). IEEE.

      [9] Yin Z, Fei Z, Yang C, Chen A. A novel SVM-RFE based biomedical data processing approach: Basic and beyond. InIndustrial Electronics Society, IECON 2016-42nd Annual Conference of the IEEE 2016 Oct 23 (pp. 7143-7148). IEEE..

      [10] Huang MW, Chen CW, Lin WC, Ke SW, Tsai CF. SVM and SVM ensembles in breast cancer prediction. PloS one. 2017 Jan 6;12(1):e0161501.

      [11] Jouni H, Issa M, Harb A, Jacquemod G, Leduc Y. Neural Network architecture for breast cancer detection and classification. InMultidisciplinary Conference on Engineering Technology (IMCET), IEEE International 2016 Nov 2 (pp. 37-41). IEEE

      [12] Nachaliel E, Lenington S, inventors; Mirabel Medical Ltd, assignee. Breast cancer detection. United States patent US 7,409,243. 2008 Aug 5.

      [13] Paulin F, Santhakumaran A. Classification of breast cancer by comparing back propagation training algorithms. International Journal on Computer Science and Engineering. 2011 Jan;3(1):327-32.

      [14] Menaka K, Karpagavalli S. Breast Cancer Classification using Support Vector Machine and Genetic Programming. International Journal of Innovative Research in Computer and Communication Engineering. 2013 Sep;1(7)

      [15] Lafta HA, Ayoob NK. Breast Cancer Diagnosis Using Genetic Fuzzy Rule Based System. Journal of University of Babylon. 2013;21(4):1109-20.

      [16] Utomo CP, Kardiana A, Yuliwulandari R. Breast cancer diagnosis using artificial neural networks with extreme learning techniques. International Journal of Advanced Research in Artificial Intelligence. 2014 Jul;3(7):10-4.

      [17] JalilAddehb ,MassoudPourmandia,†Breast Cancer Diagnosis Using Fuzzy Feature and Optimized Neural Network via the Gbest-Guided Artificial Bee Colony Algorithmâ€, Computational Research Progress in Applied Science & Engineering, Vol.1, No. 4, 152-159, 2015.

      [18] Pourmandi M, Addeh J. Breast cancer diagnosis using fuzzy feature and optimized neural network via the Gbest-guided artificial bee colony algorithm. Computational Research Progress in Applied Science & Engineering. 2015;1(4):152-9.

      [19] Naser MA, Hasan ZF, Hussein EA. A hybrid Genetic K-Means Algorithm forFeatures Selection to Classify Medical Datasets. journal of kerbala university. 2016(المؤتمر العلمي الرابع لكلية العلوم):139-49.‎

      [20] Kalpana K and Anil Arora A, Breast Cancer Diagnosis using Artificial Neural Network, (IJLTET),7(2), 2016.

      [21] Bro R, Smilde AK. Principal component analysis. Analytical Methods. 2014;6(9):2812-31.

      [22] Kotu V and Deshpande B 2015 Predictive Analytics and Data Mining (Waltham: Morgan Kaufmann)

      [23] Kavitha R and Kannan E 2016 An efficient framework for heart disease classification using feature extraction and feature selection technique in data mining IEEE Int. Conf. on Emerging Trends in Engineering Technology and Science (ICETETS) pp 1-5

      [24] Jolliffe I T 2002 Principal Component Analysis 2 nd Ed. (New York: Springer-Verlag)

      [25] Johnson RA and Wichern DW 2007 Applied Multivariate Statistical Analysis 6 th Ed. (New Jersey: Pearson Prentice Hall)

      [26] Sahu B. A Combo Feature Selection Method(Filter +Wrapper) for Microarray Gene Classification, International Journal of Pure and Applied Mathematics Volume 118 No. 16 2018

  • Downloads

  • How to Cite

    Sahu, B., Dash, S., Nandan Mohanty, S., & Kumar Rout, S. (2018). Ensemble Comparative Study for Diagnosis of Breast Cancer Datasets. International Journal of Engineering & Technology, 7(4.15), 281-285. https://doi.org/10.14419/ijet.v7i4.15.23007