Computational Measure of Cancer Using Data Mining and Optimization

  • Ashutosh Kumar DubeyEmail author
  • Umesh Gupta
  • Sonal Jain
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 39)


In this paper approaches for cancer prediction through computational measures has been discussed and analyzed. This paper provides the basis of worldwide cancer impact, methodological study with discussion, attributes and parametric impact, gaps analyzed, and the suggested computational solutions. This paper also explores the impact and the association measures of the influencing factors. The methods covered in this study are from data mining and optimization. The latest trends in the methods used and applicability have been discussed with the gaps.


Computational measure Data mining Optimization Cancer prediction 


  1. 1.
    Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68(6), 394–424 (2018)CrossRefGoogle Scholar
  2. 2.
    Ferlay, J., Colombet, M., Soerjomataram, I., Mathers, C., Parkin, D.M., Piñeros, M., Znaor, A., Bray, F.: Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int. J. Cancer 144(8), 1941–1953 (2019)CrossRefGoogle Scholar
  3. 3.
    Ferlay, J., Ervik, M., Lam, F., Colombet, M., Mery, L., Piñeros, M., Znaor, A., Soerjomataram, I., Bray, F.: Global Cancer Observatory: Cancer Today. International Agency for Research on Cancer, Lyon (2018). Accessed 20 Mar 2019
  4. 4.
    Dubey, A.K., Gupta, U., Jain, S.: Epidemiology of lung cancer and approaches for its prediction: a systematic review and analysis. Chin. J. Cancer 35(1), 71 (2016)CrossRefGoogle Scholar
  5. 5.
    Dubey, A.K., Gupta, U., Jain, S.: Breast cancer statistics and prediction methodology: a systematic review and analysis. Asian Pac. J. Cancer Prev. 16(10), 4237–4245 (2015)CrossRefGoogle Scholar
  6. 6.
    Dubey, A.K., Gupta, U., Jain, S.: Analysis of k-means clustering approach on the breast cancer Wisconsin dataset. Int. J. Comput. Assist. Radiol. Surg. 11(11), 2033–2047 (2016)CrossRefGoogle Scholar
  7. 7.
    Dubey, A.K., Gupta, U., Jain, S.: A survey on breast cancer scenario and prediction strategy. In: Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications, pp. 367–375. Springer, Cham (2015)Google Scholar
  8. 8.
    Dubey, A.K., Gupta, U., Jain, S.: Comparative study of K-means and fuzzy C-means algorithms on the breast cancer data. Int. J. Adv. Sci. Eng. Inf. Technol. 8(1), 18–29 (2018)CrossRefGoogle Scholar
  9. 9.
    Wang, Z., Li, M., Wang, H., Jiang, H., Yao, Y., Zhang, H., Xin, J.: Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features. IEEE Access 7, 105146–105158 (2019)CrossRefGoogle Scholar
  10. 10.
    Hussain, L., Aziz, W., Alshdadi, A.A., Nadeem, M.S., Khan, I.R.: Analyzing the dynamics of lung cancer imaging data using refined fuzzy entropy methods by extracting different features. IEEE Access 7, 64704–64721 (2019)CrossRefGoogle Scholar
  11. 11.
    Wu, J., Guan, P., Tan, Y.: Diagnosis and data probability decision based on non-small cell lung cancer in medical system. IEEE Access 7, 44851–44861 (2019)CrossRefGoogle Scholar
  12. 12.
    Delen, D.: Analysis of cancer data: a data mining approach. Expert Syst. 26(1), 100–112 (2009)CrossRefGoogle Scholar
  13. 13.
    Chan, C.H., Huang, T.T., Chen, C.Y., Lee, C.C., Chan, M.Y., Chung, P.C.: Texture-map-based branch-collaborative network for oral cancer detection. IEEE Trans. Biomed. Circ. Syst. 13(4), 766–780 (2019)CrossRefGoogle Scholar
  14. 14.
    Gawade, P., Chauhan, R.P.: Detection of lung cancer using image processing techniques. Int. J. Adv. Technol. Eng. Explor. 3(25), 217 (2016)CrossRefGoogle Scholar
  15. 15.
    Kawashima, K., Bai, W., Quan, C.: Text mining and pattern clustering for relation extraction of breast cancer and related genes. In: International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp. 59–63. IEEE (2017)Google Scholar
  16. 16.
    Lemsara, A., Ouadfel, S., Batouche, M.: Multi-view clustering with local refinement for cancer patient stratification. In: Intelligent Systems and Computer Vision, pp. 1–5. IEEE (2017)Google Scholar
  17. 17.
    Li, C.G., You, C., Vidal, R.: Structured sparse subspace clustering: a joint affinity learning and subspace clustering framework. IEEE Trans. Image Process. 26(6), 2988–3001 (2017)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Behera, N., Sinha, S., Gupta, R., Geoncy, A., Dimitrova, N., Mazher, J.: Analysis of gene expression data by evolutionary clustering algorithm. In: International Conference on Information Technology, pp. 165–169. IEEE (2017)Google Scholar
  19. 19.
    Govinda, K., Singla, K., Jain, K.: Fuzzy based uncertainty modeling of cancer diagnosis system. In: International Conference on Intelligent Sustainable Systems, pp. 740–743. IEEE (2017)Google Scholar
  20. 20.
    Hossain, E., Rahaman, M.A.: Bone cancer detection classification using fuzzy clustering neuro fuzzy classifier. In: International Conference on Electrical Engineering and Information & Communication Technology, pp. 541–546. IEEE (2018)Google Scholar
  21. 21.
    Fathurahman, M., Veritawati, I., Wasito, I.: Experimental analysis of iterative-scaling fuzzy additive spectral clustering (is-FADDIS) for cancer subtypes identification. In: International Conference on Advanced Computer Science and Information Systems, pp. 435–440. IEEE (2018)Google Scholar
  22. 22.
    Gupta, P., Malhi, A.K.: Using deep learning to enhance head and neck cancer diagnosis and classification. In: International Conference on System, Computation, Automation and Networking, pp. 1–6. IEEE (2018)Google Scholar
  23. 23.
    Mons, U., Gredner, T., Behrens, G., Stock, C., Brenner, H.: Cancers due to smoking and high alcohol consumption: estimation of the attributable cancer burden in Germany. Deutsches Ärzteblatt Int. 115(35–36), 571 (2018)Google Scholar
  24. 24.
    Thankappan, K.R., Thresia, C.U.: Tobacco use & social status in Kerala. Indian J. Med. Res. 126(4), 300 (2007)Google Scholar
  25. 25.
    Gajalakshmi, V., Peto, R., Kanaka, T.S., Jha, P.: Smoking and mortality from tuberculosis and other diseases in India: retrospective study of 43 000 adult male deaths and 35 000 controls. The Lancet 362(9383), 507–515 (2003)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ashutosh Kumar Dubey
    • 1
    Email author
  • Umesh Gupta
    • 1
  • Sonal Jain
    • 1
  1. 1.Institute of Engineering and TechnologyJK Lakshmipat UniversityJaipurIndia

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