Abstract
In medical domain, prognosis prediction treated as a regression problem is generally applied to predict the event duration time, such as the duration time of the recurrence of a certain disease. Recently, machine learning techniques are gaining popularity in this field because of its effectiveness and reliability. In this paper, a method based on support vector machine (SVM) to predict the exact recurrence time has been proposed. The method is compared with other four prognostic methods using Wisconsin Breast Cancer Dataset. Experimental results demonstrate that the method is more simplified to be implemented than the other four prognostic methods, and it performs much better than the medium level.
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References
Xu R, Cai X, Wunsch DCII (2006) Gene expression data for DLBCL cancer survival prediction with a combination of machine learning technologies. In: IEEE-EMBS 2005. 27th annual international conference of the engineering in medicine and biology society Jan 2005, IEEE pp 894–897
Jerez-Aragonés JM, Gómez-Ruiz JA, Ramos-Jiménez G, Muñoz-Pérez J, Alba-Conejo E (2003). A combined neural network and decision trees model for prognosis of breast cancer relapse. Artif Intell Med 27(1):45–63
Mangasarian OL, Street WN, Wolberg WH (1995) Breast cancer diagnosis and prognosis via linear programming. Oper Res 43(4):570–577
Shatkay TH, Chan HWY (2006, May). Breast cancer prognosis via Gaussian mixture regression. In: CCECE’06, Canadian conference on electrical and computer engineering, May 2006, IEEE pp 987–990
Bagotskaya N, Lossev I, Losseva N, Parakhin M (2005) Prediction of time to event for censored data: ridge regression with linear constraints in kernel space. In: IJCNN’05. Proceedings EEE International Joint Conference on Neural Networks 2005, IEEE, vol 2 pp 1033–1038
Shivaswamy PK, Chu W, Jansche M (2007, October). A support vector approach to censored targets. In: ICDM Seventh IEEE International Conference on Data Mining, Oct 2007, IEEE, pp 655–660
Sun BY, Zhu ZH, Li J, Linghu B (2011) Combined Feature Selection and Cancer Prognosis Using Support Vector Machine Regression. IEEE/ACM Trans Comput Biol Bioinf (TCBB), 8(6):1671–1677
Vapnik V (1999) The nature of statistical learning theory. Springer
Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach learn 46(1–3):389–422
Hsu CW, Chang CC, Lin CJ (2003). A practical guide to support vector classification
The Center for Machine Learning and Intelligent Systems at the University of California, Irvine. URL:http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/
Street WN (1994) Cancer diagnosis and prognosis via linear-programming-based machine learning. Oper Res 43(4):570–577
Ling Z (2002) The relationship between kernel functions based SVM and three-layer feedforward neural networks. Chin J Comput 25(7):1–5
Acknowledgments
The authors would like to thank professor Li’s at University of South Australia for his constructive comments. This project is supported by national natural science funding of China (project number: 11265007).
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Qinan, J., Lei, M., Jianfeng, H., QingQing, Y., Jun, Z. (2014). A Primary Study for Cancer Prognosis based on Classification and Regression Using Support Vector Machine. In: Li, S., Jin, Q., Jiang, X., Park, J. (eds) Frontier and Future Development of Information Technology in Medicine and Education. Lecture Notes in Electrical Engineering, vol 269. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7618-0_89
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DOI: https://doi.org/10.1007/978-94-007-7618-0_89
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