Infection status outcome, machine learning method and virus type interact to affect the optimised prediction of hepatitis virus immunoassay results from routine pathology laboratory assays in unbalanced data

BACKGROUND: Advanced data mining techniques such as decision trees have been successfully used to predict a variety of outcomes in complex medical environments. Furthermore, previous research has shown that combining the results of a set of individually trained trees into an ensemble-based classifie...

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Detalles Bibliográficos
Autores principales: Richardson, Alice M, Lidbury, Brett A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3697984/
https://www.ncbi.nlm.nih.gov/pubmed/23800244
http://dx.doi.org/10.1186/1471-2105-14-206

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3697984/
https://www.ncbi.nlm.nih.gov/pubmed/23800244
http://dx.doi.org/10.1186/1471-2105-14-206

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