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

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.


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