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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

Richardson, Alice; Lidbury, Brett A

Description

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 classifier can improve overall classification accuracy. This paper investigates the effect of data pre-processing, the use of ensembles constructed by bagging, and a simple majority vote to combine...[Show more]

CollectionsANU Research Publications
Date published: 2013-06-25
Type: Journal article
URI: http://hdl.handle.net/1885/16923
Source: BMC Bioinformatics
DOI: 10.1186/1471-2105-14-206

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