Open AccessCANCER PROGNOSTIC EVALUATION VIA SUPPORT VECTOR MACHINES
Author(s) -
Domenico Conforti,
Domenico Costanzo,
Rosita Guido
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.3.3.302
Subject(s) - support vector machine , computer science , artificial intelligence , machine learning , robustness (evolution) , binary classification , disease , medicine , biochemistry , chemistry , gene
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.