Advertisement

A Prediction Survival Model Based on Support Vector Machine and Extreme Learning Machine for Colorectal Cancer

  • PreetiEmail author
  • Rajni Bala
  • Ram Pal Singh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 887)

Abstract

Colorectal cancer is the third largest cause of cancer deaths in men and second most common in women worldwide. In this paper, a prediction model based on Support Vector Machine (SVM) and Extreme Learning Machine (ELM) combined with feature selection has been developed to estimate colorectal-cancer-specific survival after 5 years of diagnosis. Experiments have been conducted on dataset of Colorectal Cancer patients publicly available from Surveillance, Epidemiology, and End Results (SEER) program. The performance measures used to evaluate proposed methods are classification accuracy, F-score, sensitivity, specificity, positive and negative predictive values and receiver operating characteristic (ROC) curves. The results show very good classification accuracy for 5-year survival prediction for the SVM and ELM model with 80%–20% partition of data with 16 number of features and this is very promising as compared to existing learning models result.

Keywords

Colorectal cancer Extreme Learning Machine (ELM) Feature selection Survival prediction Surveillance\(, \) Epidemiology\(, \) and End Results (SEER) Support Vector Machine (SVM) 

References

  1. 1.
    Fathy, S.K.: A predication survival model for colorectal cancer. In: Proceedings of the 2011 American Conference on Applied Mathematics and the 5th WSEAS International Conference on Computer Engineering and Applications, ser. AMERICAN-MATH’11/CEA’11, pp. 36–42. World Scientific and Engineering Academy and Society (WSEAS), Stevens Point (2011)Google Scholar
  2. 2.
    Burke, H.B., Goodman, P.H., Rosen, D.B., Henson, D.E., Weinstein, J.N., Harrell, F.E., Marks, J.R., Winchester, D.P., Bostwick, D.G.: Artificial neural networks improve the accuracy of cancer survival prediction. Cancer 79(4), 857–862 (1997)CrossRefGoogle Scholar
  3. 3.
    Gao, P., Zhou, X., Wang, Z.-N., Song, Y.-X., Tong, L.-L., Xu, Y.-Y., Yue, Z.-Y., Xu, H.-M.: Which is a more accurate predictor in colorectal survival analysis? Nine data mining algorithms vs. the TNM staging system. PLOS ONE 7(7), 1–8 (2012)Google Scholar
  4. 4.
    Saleema, J.S., Bhagawathi, N., Monica, S., Shenoy, P.D., Venugopal, K.R., Patnaik, L.M.: Cancer prognosis prediction using balanced stratified sampling. CoRR, vol. abs/1403.2950 (2014)Google Scholar
  5. 5.
    Saleema, J.S., Sairam, B., Naveen, S.D., Yuvaraj, K., Patnaik, L.M.: Prominent label identification and multi-label classification for cancer prognosis prediction. In: TENCON 2012 IEEE Region 10 Conference, pp. 1–6 (2012)Google Scholar
  6. 6.
    Berger, A.C., Sigurdson, E.R., LeVoyer, T., Hanlon, A., Mayer, R.J., Macdonald, J.S., Catalano, P.J., Haller, D.G.: Colon cancer survival is associated with decreasing ratio of metastatic to examined lymph nodes. J. Clin. Oncol. 23(34), 8706–8712 (2005)CrossRefGoogle Scholar
  7. 7.
    Swanson, R.S., Compton, C.C., Stewart, A.K., Bland, K.I.: The prognosis of T3N0 colon cancer is dependent on the number of lymph nodes examined. Ann. Surg. Oncol. 10, 65–71 (2003)CrossRefGoogle Scholar
  8. 8.
    Al-Bahrani, R., Agrawal, A., Choudhary, A.: Colon cancer survival prediction using ensemble data mining on SEER data. In: IEEE International Conference on Big Data, pp. 9–16 (2013)Google Scholar
  9. 9.
    O’Connell, J.B., Maggard, M.A., Ko, C.Y.: Colon cancer survival rates with the new American joint committee on cancer sixth edition staging. JNCI J. Natl. Cancer Inst. 96(19), 14–20 (2004)CrossRefGoogle Scholar
  10. 10.
    Lundin, M., Lundin, J., Burke, H., Toikkanen, S., Pylkkänen, L., Joensuu, H.: Artificial neural networks applied to survival prediction in breast cancer. Oncology 57(4), 281–286 (1999)CrossRefGoogle Scholar
  11. 11.
    Emery, R., Wang, S.J., Fuller, C.D., Thomas, C.R.: Conditional survival in rectal cancer: a seer database analysis. Gastrointest. Cancer Res. (GCR) 1(3), 84–89 (2007)Google Scholar
  12. 12.
    Endo, T.S.A., Tanaka, H.: Comparison of seven algorithms to predict breast cancer survival(\(<\)special issue\(>\) contribution to 21 century intelligent technologies and bioinformatics). Biomed. Fuzzy Hum. Sci. Off. J. Biomed. Fuzzy Syst. Assoc. 13(2), 11–16 (2008)Google Scholar
  13. 13.
    Fradkin, D., Schneider, D., Muchnik, I.: Machine learning methods in the analysis of lung cancer survival data. DIMACS Technical report 2005–35 (2006)Google Scholar
  14. 14.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press (2000)Google Scholar
  15. 15.
    Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006)CrossRefGoogle Scholar
  16. 16.
    Rao, C.R., Mitra, S.K.: Generalized inverse of matrices and its applications (1971)Google Scholar
  17. 17.
    Hall, M.A.: Correlation-based feature selection for machine learning (1999)Google Scholar
  18. 18.
    Bradley, A.P.: The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognit. 30(7), 1145–1159 (1997)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceDeen Dayal Upadhyaya College, University of DelhiDelhiIndia

Personalised recommendations