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

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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

2004, Volume 3, Issue 3

Section

Articles

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