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 measure- ments 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.
RBF kernel method and its applications to clinical data
Perracchione E., Stura I. (2016) "RBF kernel method and its applications to clinical data " Dolomites Research Notes on Approximation, 9(Special_Issue), 13-18. DOI: 10.14658/PUPJ-DRNA-2016-Special_Issue-3
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Dolomites Research Notes on Approximation
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