RBF kernel method and its applications to clinical data

Abstract

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.

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  
Year of Publication
2016
Journal
Dolomites Research Notes on Approximation
Volume
9
Issue Number
Special_Issue
Start Page
13
Last Page
18
Date Published
09/2016
ISSN Number
2035-6803
Serial Article Number
3
DOI
10.14658/PUPJ-DRNA-2016-Special_Issue-3
Issue
Section
SpecialIssue