RBF kernel method and its applications to clinical data. (English) Zbl 1372.65033

Perracchione, Emma; Stura, Ilaria

Dolomites Res. Notes Approx. 9, Spec. Iss., 13-18 (2016).

Summary: 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.

MSC:

65D05	Numerical interpolation
41A05	Interpolation in approximation theory
41A30	Approximation by other special function classes
92B15	General biostatistics
92C50	Medical applications (general)