RBF kernel method and its applications to clinical data
Emma Perracchione, Ilaria Stura · Institutional Research Information System University of Turin (University of Turin) · 2016
In this paper, basing our considerations on kernel-based approaches, we propose a new strategy allowing to approximate the prostate cancer dynamics.In particular, starting from several measurements of a specific biomarker, we estimate the tumor growth rate.To achieve this aim, we pre-process data via Radial Basis Function (RBF) interpolation.A careful choice of the basis function and of its shape parameter enables us to obtain reliable approximations of the cancer evolution.Numerical evidence supports our findings.