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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

Lidbury, B.A.; Richardson, Alice

Description

Background: Data mining techniques such as support vector machines (SVMs) have been successfully used to predict outcomes for complex problems, including for human health. Much health data is imbalanced, with many more controls than positive cases. Methods: The impact of three balancing methods and one feature selection method is explored, to assess the ability of SVMs to classify imbalanced diagnostic pathology data associated with the laboratory diagnosis of hepatitis B (HBV) and hepatitis C...[Show more]

CollectionsANU Research Publications
Date published: 2017
Type: Journal article
URI: http://hdl.handle.net/1885/138122
DOI: 10.1186/s12911-017-0522-5

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