Skip to main content
SpringerLink
Log in

An intelligent method of cancer prediction based on mobile cloud computing

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In recent years, mobile medical services have gradually become the common focus of the communications and healthcare industries. People can get medical information and services efficiently and conveniently anytime, anywhere. However, due to the limitations of the mobile terminal’s own computing and storage capabilities, mobile therapy is greatly challenged. Therefore, we propose a mobile medical health system based on cloud computing. Firstly, principal component analysis was used to obtain representative features. Then, a simplified feature subset was applied to support vector machine (SVM) based on Sigmoid kernel function. The data set was categorized by SVM as cancer patient and normal object experimental results show that the method is improved in accuracy, sensitivity, and specificity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Siegel, R.L., Miller, K.D., Fedewa, S.A., Ahnen, D.J., Meester, R.G., Barzi, A., Jemal, A.: Colorectal cancer statistics, 2017. CA 67(3), 177–193 (2017)

    Google Scholar 

  2. Chen, W., Zheng, R., Baade, P.D., Zhang, S., Zeng, H., Bray, F., Jemal, A., Yu, X.Q., He, J.: Cancer statistics in china, 2015. CA 66(2), 115–132 (2016)

    Google Scholar 

  3. Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2016. CA 66(1), 7–30 (2016)

    Google Scholar 

  4. Miller, K.D., Siegel, R.L., Lin, C.C., Mariotto, A.B., Kramer, J.L., Rowland, J.H., Stein, K.D., Alteri, R., Jemal, A.: Cancer treatment and survivorship statistics, 2016. CA 66(4), 271–289 (2016)

    Google Scholar 

  5. Wang, L., Laszewski, G.V., Younge, A., He, X., Kunze, M., Tao, J., Fu, C.: Cloud computing: a perspective study. New Gener. Comput. 28(2), 137–146 (2010)

    Article  Google Scholar 

  6. Dinh, H.T., Lee, C., Niyato, D., Wang, P.: A survey of mobile cloud computing: architecture, applications, and approaches. Wirel. Commun. Mob. Comput 13(18), 1587–1611 (2013)

    Article  Google Scholar 

  7. Maglogiannis, I., Doukas, C., Kormentzas, G., Pliakas, T.: Wavelet-based compression with roi coding support for mobile access to dicom images over heterogeneous radio networks. IEEE Trans. Inf. Technol. Biomed. 13(4), 458–66 (2009)

    Article  Google Scholar 

  8. Garro, B.A., Rodríguez, K., Vázquez, R.A.: Classification of dna microarrays using artificial neural networks and abc algorithm. Appl. Soft Comput. 38, 548–560 (2016)

    Article  Google Scholar 

  9. Li, J.: A new robust signal recognition approach based on holder cloud features under varying snr environment. Ksii Trans. Internet Inf. Syst. 9(12), 4934–4949 (2015)

    Google Scholar 

  10. Zou, Y., Zhu, J., Yang, L., Liang, Y.C.: Securing physical-layer communications for cognitive radio networks. Commun. Mag. IEEE 53(9), 48–54 (2015)

    Article  Google Scholar 

  11. Ding, G., Wang, J., Wu, Q., Yao, Y.D., Song, F., Tsiftsis, T.A.: Cellular-base-station-assisted device-to-device communications in tv white space. IEEE J. Sel. Areas Commun. 34(1), 107–121 (2015)

    Article  Google Scholar 

  12. Wang, G., Zhao, Y., Huang, J., Wang, W.: The controller placement problem in software defined networking: a survey. IEEE Netw. 31(5), 21–27 (2017)

    Article  Google Scholar 

  13. Liu, S., Fu, W., He, L., Zhou, J., Ma, M.: Distribution of primary additional errors in fractal encoding method. Multimed. Tools Appl. 76(4), 5787–5802 (2017)

    Article  Google Scholar 

  14. Doukas, C., Pliakas, T., Maglogiannis, I.: Mobile healthcare information management utilizing cloud computing and android OS. In: International Conference of the IEEE Engineering in Medicine and Biology, pp. 1037–1040 (2010)

  15. Bhat, J.A., George, V., Malik, B.: Cloud computing with machine learning could help us in the early diagnosis of breast cancer. In: International Conference on Advances in Computing and Communication Engineering (2015)

  16. Chen, H., Cheng, B.C., Liao, G.T., Kuo, T.C.: Hybrid classification engine for cardiac arrhythmia cloud service in elderly healthcare management. J. Vis. Lang. Comput. 25(6), 745–753 (2014)

    Article  Google Scholar 

  17. Lokeswari, Y.V., Jacob, S.G., Lokeswari, Y.V., Jacob, S.G.: A cloud-based data mining framework for improved clinical diagnosis through parallel classification. In: International Conference on Applied and Theoretical Computing and Communication Technology, pp. 583–588 (2016)

  18. Gatuha, G., Jiang, T.: Android based naive bayes probabilistic detection model for breast cancer and mobile cloud computing: design and implementation. Int. J. Eng. Res. Afr. 21, 197–208 (2016)

    Article  Google Scholar 

  19. Kharya, S., Agrawal, S., Soni, S.: Naive bayes classifiers: a probabilistic detection model for breast cancer. Int. J. Comput. Appl. 92(10), 26–31 (2014)

    Google Scholar 

  20. Hamsagayathri, P., Sampath, P.: Decision tree classifiers for classification of breast cancer. Int. J. Curr. Pharm. Res. 9(2), 31 (2017)

    Article  Google Scholar 

  21. Medjahed, S.A., Saadi, T.A., Benyettou, A., Ouali, M.: Kernel-based learning and feature selection analysis for cancer diagnosis. Appl. Soft Comput. 51, 39–48 (2017)

    Article  Google Scholar 

  22. Liu, S., Zheng, H., Feng, Y., Li, W.: Prostate cancer diagnosis using deep learning with 3d multiparametric mri, arXiv preprint arXiv:1703.04078, (2017)

  23. Kar, S., Majumder, D.D.: An investigative study on early diagnosis of prostate cancer using neuro-fuzzy classification system for pattern recognition. Int. J. Fuzzy Syst. 19(2), 423–439 (2017)

    Article  Google Scholar 

  24. Badria, F.A., Shoaip, N., Elmogy, M., Riad, A., Zaghloul, H.: A framework for ovarian cancer diagnosis based on amino acids using fuzzy-rough sets with svm. In: International Conference on Advanced Machine Learning Technologies and Applications, pp. 389–400, Springer (2014)

  25. Jolliffe, I.T.: Principal component analysis and factor analysis, In: Principal Component Analysis, pp. 115–128, Springer (1986)

  26. Asuncion, A., Newman, D.: Uci machine learning repository (2007)

Download references

Acknowledgements

This work is supported by the National Science Foundation of China (Grant Nos. 61501132, 61771154, 61370084), the China Postdoctoral Science Foundation No. 2016M591515, the Heilongjiang Postdoctoral Sustentation Fund with No. LBH-Z14055, Harbin Application Technology Research and Development Project (Grant Nos. 2016RAQXJ063, 2016RAXXJ013).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Harbin Engineering University, Harbin, China

    Guoyin Zhang, Liang Kou, Ye Yuan, Jianguo Sun, Qingan Da & Wenshan Wang

  2. College of Information and Communication Engineering, Harbin Engineering University, Harbin, China

    Yun Lin

Authors
  1. Guoyin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Kou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ye Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianguo Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yun Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qingan Da
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wenshan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yun Lin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, G., Kou, L., Yuan, Y. et al. An intelligent method of cancer prediction based on mobile cloud computing. Cluster Comput 22 (Suppl 5), 11527–11535 (2019). https://doi.org/10.1007/s10586-017-1416-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1416-0

Keywords

Search

Navigation