CANCER PROGNOSTIC EVALUATION VIA SUPPORT VECTOR MACHINES

Authors

  • Domenico Conforti
  • Domenico Costanzo
  • Rosita Guido

DOI:

https://doi.org/10.47839/ijc.3.3.302

Keywords:

Medical Decision Making, Breast Cancer Prognosis, Support Vector Machine, Kernel Functions

Abstract

In this paper we considered a very challenging medical decision making problem: the short-term prognosis evaluation of breast cancer patients. In particular, the oncologist has to predict the more likely outcome of the disease in terms of survival or recurrence after a given follow-up period: “good” prognosis if the patient is still alive and has not recurrence after the follow-up period, “poor” prognosis if the patient has recurrence or dies within the follow-up period. This prediction can be realized on the basis of the execution of specific clinical tests and patient examinations. The relevant medical decision making problem has been formulated as a supervised binary classification problem. By the framework of generalized Support Vector Machine models, we tested and validate the behavior of four kernel based classifiers: Linear, Polynomial, Gaussian and Laplacian. The overall results demonstrate the effectiveness and robustness of the proposed approaches for solving the relevant medical decision making problem.

References

Y. J. Lee, O. L. Mangasarian, W. H. Wolberg, Survival-Time Classification of Breast Cancer Patients, Computational Optimization and Applications 25 (2003), pp. 151-166.

M. De Laurentiis, S. De Placido, A. R. Bianco, G. M. Clark, P. M. Ravdin, A Prognostic Model that makes quantitative estimates of the Probability of Relapse for Breast Cancer Patients, Clinical Cancer Research 5 (1999). pp. 4133-4139.

W. H. Wolberg, W. N. Street, D. M. Heisey, O. L. Mangasarian, Computer-derived Nuclear Grade and Breast Cancer Prognosis, Anal. Quant. Cytol. Histol. 17 (4) (1995), pp. 257-264.

M. Ferno, Prognostic factors in breast cancer: a brief review, Anticancer Research 18 (3C) (1998), pp. 2167-2171.

V. N. Vapnik, Statistical Learning Theory, John Wiley & Sons, New York, 1998.

V. Cherkassky, F. Mulier, Learning from Data - Concepts, Theory and Methods, John Wiley & Sons, New York, 1998.

N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, 2000.

O.L. Mangasarian, Generalized Support Vector Machines, Advanced in Large Margin Classifiers, A.J. Smola, P. Barlett, B. Scholkopf, D. Schuurmans, eds., MIT Press, Cambridge, Massachusetts, 2000, pp. 135-146.

J. Schurmann, Pattern Classification: a unified view of statistical and neural approaches, John Wiley & Sons, New York, 1996.

M.A. Aizerman, E’.M. Braverman, L.I. Rozonoer, Theoretical foundations of the Potential Function method in Pattern Recognition Learning, Automation and Remote Control 25 (1964) pp. 821-837.

B. Scholkopf, A. J. Smola, Learning with Kernels, MIT Press, Cambridge, 2002.

M. Stone, Cross-validation choices and assessment of statistical predictions, Journal of the Royal Statistical Society, Series B, 36 (1974) pp. 111-147.

CPLEX, ILOG CPLEX 6.5: User's Manual, CPLEX Optimization, Inc., Incline Village, NV, 1999.

Downloads

Published

2014-08-01

How to Cite

Conforti, D., Costanzo, D., & Guido, R. (2014). CANCER PROGNOSTIC EVALUATION VIA SUPPORT VECTOR MACHINES. International Journal of Computing, 3(3), 29-34. https://doi.org/10.47839/ijc.3.3.302

Issue

Section

Articles