Training strategies for Radial Basis Function Neural Networks: A study applied to prostate cancer prognosis

A development of a Radial Basis Function Neural Network applied to the prostate cancer prognosis is presented. Five training strategies were implemented: the Successive Approximation algorithm; the k-means algorithm; Ainet; Ainet + k-means, and Successive Approximation + k-means. Comparing all tested strategies, the Successive Approximation training strategy obtained the best fitness measure. And comparing this result to other previously studies, we concluded that the use of Radial Basis Function Neural Networks becomes a viable alternative to the prostate cancer prognosis.