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.
Early Recognition and Discrimination of COVID-19 Severity using Slime Mould Support Vector Machine for Medical Decision-making