Combining elemental analysis of toenails and machine learning techniques as a non-invasive diagnostic tool for the robust classification of type-2 diabetes.
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%0 Journal Article
%1 journals/eswa/CarterLSSD19
%A Carter, Jake A.
%A Long, Christina S.
%A Smith, Beth P.
%A Smith, Thomas L.
%A Donati, George L.
%D 2019
%J Expert Syst. Appl.
%K dblp
%P 245-255
%T Combining elemental analysis of toenails and machine learning techniques as a non-invasive diagnostic tool for the robust classification of type-2 diabetes.
%U http://dblp.uni-trier.de/db/journals/eswa/eswa115.html#CarterLSSD19
%V 115
@article{journals/eswa/CarterLSSD19,
added-at = {2019-10-19T00:00:00.000+0200},
author = {Carter, Jake A. and Long, Christina S. and Smith, Beth P. and Smith, Thomas L. and Donati, George L.},
biburl = {https://www.bibsonomy.org/bibtex/2cf2bf152de78d3d6238ff1d8f6d91778/dblp},
ee = {https://doi.org/10.1016/j.eswa.2018.08.002},
interhash = {0993b9dc84c43162b7eb58ed113fd7ba},
intrahash = {cf2bf152de78d3d6238ff1d8f6d91778},
journal = {Expert Syst. Appl.},
keywords = {dblp},
pages = {245-255},
timestamp = {2019-10-22T11:42:00.000+0200},
title = {Combining elemental analysis of toenails and machine learning techniques as a non-invasive diagnostic tool for the robust classification of type-2 diabetes.},
url = {http://dblp.uni-trier.de/db/journals/eswa/eswa115.html#CarterLSSD19},
volume = 115,
year = 2019
}
