A novel approach for breast cancer prediction using optimized ANN classifier based on big data environment

  • M. SupriyaEmail author
  • A. J. Deepa


Cancer is caused by the un-controlled division of abnormal cells in a body part. Various cancers exist in this world and one amongst them is breast cancer. Breast cancer (BC) threatens the lives of people and today, it is the secondary prime cause of death in women. Numerous research directions concentrated on the prediction of BC. The prevailing prediction model is time-consuming and have less accuracy. To trounce those drawbacks, this paper proposed a BC prediction system (BCPS) utilizing Optimized Artificial Neural Network (OANN). Primarily, the unprocessed BC data are regarded as the input. The big data (BD) storage comprises some repeated information. Secondarily, such repeated data are eliminated by utilizing Hadoop MapReduce. In the subsequent stage, the data are preprocessed utilizing replacing of missing attributes (RMA) and normalization techniques. Subsequently, the features are generally chosen by utilizing Modified Dragonfly algorithm (MDF). Then, the selected features are inputted for classification. Here, it classifies the features utilizing OANN. Optimization is done by employing the Gray Wolf Optimization (GWO) algorithm. Experiential outcomes are contrasted with prevailing IWDT (Improved Weighted-Decision Tree) in respect of precision, recall, accuracy, and ROC.


Optimized artificial neural network (OANN) Modified dragonfly algorithm (MDF) Gray wolf optimization (GWO) Improved weighted decision tree (IWDT) Big data 



  1. 1.
    Priyanga A, Prakasam S (2013) Effectiveness of data mining-based cancer prediction system (DMBCPS). Int J Comput Appl 83(10)CrossRefGoogle Scholar
  2. 2.
    Ramachandran P, Girija N, Bhuvaneswari T (2014) Early detection and prevention of cancer using data mining techniques. Int J Comput Appl 97(13)CrossRefGoogle Scholar
  3. 3.
    Meera C, Nalini D (2018) Breast cancer prediction system using data mining methods. International Journal of Pure and Applied Mathematics 119(12):10901–10911Google Scholar
  4. 4.
    Nakte J, Himmatramka V (2016) Breast cancer prediction using data mining techniques. International Journal on Recent and Innovation Trends in Computing and Communication 4(11):55–60Google Scholar
  5. 5.
    Priyanga A, Prakasam S (2013) The role of data mining-based cancer prediction system (DMBCPS) in Cancer Awareness. International Journal of Computer Science and Engineering Communications (IJCSEC) 1(1)Google Scholar
  6. 6.
    Gupta P, Shalini L (2018) Analysis of machine learning techniques for breast cancer prediction. International Journal of Engineering and Computer Science 7(05):23891–23895Google Scholar
  7. 7.
    Arutchelvan K, Periyasamy R (2015) Cancer prediction system using datamining techniques. International Research Journal of Engineering and Technology (IRJET) 02(08)Google Scholar
  8. 8.
    Yue W, Wang Z, Chen H, Payne A, Liu X (2018) Machine learning with applications in breast cancer diagnosis and prognosis. Designs 2(2):13CrossRefGoogle Scholar
  9. 9.
    Entezari-Maleki R, Rezaei A, Minaei-Bidgoli B (2009) Comparison of classification methods based on the type of attributes and sample size. Journal of Convergence Information Technology 4(3):94–102CrossRefGoogle Scholar
  10. 10.
    Baraa M. Abed, Khalid Shaker, Hamid A Jalab, Hothefa Shaker, Ali Mohammed Mansoor, Ahmad F. Alwan, and Ihsan Salman Al-Gburi, “A hybrid classification algorithm approach for breast cancer diagnosis”, In Industrial Electronics and Applications Conference (IEACon), IEEE, pp. 269–274, 2016Google Scholar
  11. 11.
    Deshmukh S, Shinde S (2015) Hypothesis on different data mining algorithms. Int J Eng Res Appl 5(12):86–91Google Scholar
  12. 12.
    Kwetishe Joro Danjuma, “Performance evaluation of machine learning algorithms in post-operative life expectancy in the lung cancer patients”, 2015Google Scholar
  13. 13.
    Vazifehdan M, Moattar MH, Jalali M (2018) A hybrid Bayesian network and tensor factorization approach for missing value imputation to improve breast cancer recurrence prediction. Journal of King Saud University-Computer and Information SciencesGoogle Scholar
  14. 14.
    Jafari-Marandi R, Davarzani S, Gharibdousti MS, Smith BK (2018) An optimum ann-based breast cancer diagnosis: bridging gaps between ANayN learning and decision-making goals. Applied Soft Computing JournalGoogle Scholar
  15. 15.
    Hamouda SKM, Wahed ME, abo Alez RH, Riad K (2018) Robust breast cancer prediction system based on rough set theory at national cancer institute of Egypt. Comput Methods Prog Biomed 153:259–268CrossRefGoogle Scholar
  16. 16.
    Kanimozhi U, Ganapathy S, Manjula D, Kannan A An intelligent risk prediction system for breast cancer using fuzzy temporal rules. National Academy Science Letters:1–6Google Scholar
  17. 17.
    Moon WK, Chen I-L, Yi A, Bae MS, Shin SU, Chang R-F Computer-aided prediction model for axillary lymph node metastasis in breast cancer using tumor morphological and textural features on ultrasound. Comput Methods Prog Biomed, 162:129–137, 2018CrossRefGoogle Scholar
  18. 18.
    Mohebian MR, Marateb HR, Mansourian M, Mañanas MA, Mokarian F (2017) A hybrid computer-aided-diagnosis system for prediction of breast cancer recurrence (HPBCR) using optimized ensemble learning. Computational and Structural Biotechnology Journal 15:75–85CrossRefGoogle Scholar
  19. 19.
    Nilashi M, Ibrahim O, Ahmadi H, Shahmoradi L (2017) A knowledge-based system for breast cancer classification using fuzzy logic method. Telematics Inform 34(4):133–144CrossRefGoogle Scholar
  20. 20.
    Sun W, Tseng T-L, Qian W, Saltzstein EC, Zheng B, Yu H, Zhou S (2017) A new near-term breast cancer risk prediction scheme based on the quantitative analysis of ipsilateral view mammograms. Comput Methods Prog BiomedGoogle Scholar
  21. 21.
    Mert A, Kılıç N, Bilgili E, Akan A (2015) breast cancer detection with reduced feature set. Computational and Mathematical Methods in MedicineGoogle Scholar
  22. 22.
    Zheng B, Yoon SW, Lam SS (2014) Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Syst Appl 41(4):1476–1482CrossRefGoogle Scholar
  23. 23.
    Aličković E, Subasi A (2017) Breast cancer diagnosis using GA feature selection and rotation Forest. Neural Comput & Applic 28(4):753–763CrossRefGoogle Scholar
  24. 24.
    Bhardwaj A, Tiwari A (2015) Breast cancer diagnosis using genetically optimized neural network model. Expert Syst Appl 42(10):4611–4620CrossRefGoogle Scholar
  25. 25.
    Cai T, He H, Zhang W (2018) Breast Cancer diagnosis using imbalanced learning and ensemble method. Applied and Computational Mathematics 7(3):146–154CrossRefGoogle Scholar
  26. 26.
    Juneja K, Rana C (2018) An improved weighted decision tree approach for breast cancer prediction. Int J Inf Technol:1–8Google Scholar
  27. 27.
    BalaAnand M, Karthikeyan N, Karthik S (2018) Designing a Framework for Communal Software: Based on the Assessment Using Relation Modelling. Int J Parallel Prog.
  28. 28.
    Maram, B., Gnanasekar, J.M., Manogaran, G, et al. SOCA (2018). 10.1007/s11761-018-0249-xGoogle Scholar
  29. 29.
    Sivaparthipan CB, Karthikeyan N, Karthik S (2018) Designing statistical assessment healthcare information system for diabetics analysis using big data. Multimed Tools ApplGoogle Scholar
  30. 30.
    BalaAnand M, Karthikeyan N, Karthik S, Sivaparthipan CB (2017) A survey on BigData with various V's on comparison of apache hadoop and apache spark. Adv Nat Appl SciGoogle Scholar
  31. 31.
    Anand MB, Sivaparthipan CB, Karthikeyan N, Karthik S Early Detection And Prediction Of Amblyopia By Predictive Analytics Using Apache Spark. International Journal of Pure and Applied Mathematics (IJPAM)- Scopus ISSN: 1314–3395 (on-line version) 119(15):3159–3171Google Scholar
  32. 32.
    Matsumoto K, Ren F, Matsuoka M, Yoshida M, Kita K (2019) Slang feature extraction by analysing topic change on social media. CAAI Transactions on Intelligence Technology 4(1):64–71CrossRefGoogle Scholar
  33. 33.
    Susan S, Agrawal P, Mittal M, Bansal S (2019) A new shape descriptor in the context of edge continuity. CAAI Transactions on Intelligence Technology Google Scholar
  34. 34.
    Ding R, Dai L, Li G, Liu H (2019) TDD-net: a tiny defect detection network for printed circuit boards. CAAI Transactions on Intelligence Technology Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Anna UniversityChennaiIndia
  2. 2.Ponjesly College of EngineeringKaniyakumariIndia

Personalised recommendations