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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Fradkin, D., Schneider, D., Muchnik, I.: Machine learning methods in the analysis of lung cancer survival data. DIMACS Technical report 2005–35 (2006)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press (2000)
Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006)
Rao, C.R., Mitra, S.K.: Generalized inverse of matrices and its applications (1971)
Hall, M.A.: Correlation-based feature selection for machine learning (1999)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Preeti, Bala, R., Singh, R.P. (2019). A Prediction Survival Model Based on Support Vector Machine and Extreme Learning Machine for Colorectal Cancer. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-030-03405-4_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-03405-4_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03404-7
Online ISBN: 978-3-030-03405-4
eBook Packages: EngineeringEngineering (R0)