Enhancement of hepatitis virus immunoassay outcome predictions in imbalanced routine pathology data by data balancing and feature selection before the application of support vector machines-Reference-Cited by-同舟云学术

Enhancement of hepatitis virus immunoassay outcome predictions in imbalanced routine pathology data by data balancing and feature selection before the application of support vector machines

Published:2017-08-14 Issue:1 Volume:17 Page:
ISSN:1472-6947
Container-title:BMC Medical Informatics and Decision Making
language:en
Short-container-title:BMC Med Inform Decis Mak

Author:

Richardson Alice M.^ORCID,Lidbury Brett A.

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Health Policy,Computer Science Applications

Link

http://link.springer.com/content/pdf/10.1186/s12911-017-0522-5.pdf

Reference32 articles.

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3. Richardson AM, Lidbury BA. Infection status outcome, machine learning method and virus type interact to affect the optimised prediction of hepatitis B and C virus immunoassay results from routine pathology laboratory assays in unbalanced data sets. BMC Bioinfo. 2013;14:206–13.

4. Vapnik V. Statistical learning theory. New York: Wiley; 1998.

5. Smola AJ, Schölkopf B. A tutorial on support vector regression. Stat Comput. 2004;14:199–222.

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