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

BMC Bioinformatics

Volumn 14, Issue 1, 2013, Pages

  Richardson, Alice M ^a,b   Lidbury, Brett A ^b  

^a UNIVERSITY OF CANBERRA   (Australia)

^b Australian National University   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

CANBERRA , AUSTRALIA; CLASSIFICATION ACCURACY; CLASSIFICATION PREDICTION; DATA PREPROCESSING; DECISION SUPPORT PROCESS; GRAPHICAL ANALYSIS; HEPATITIS C VIRUS; MACHINE LEARNING METHODS;

DECISION SUPPORT SYSTEMS; DECISION TREES; DIAGNOSIS; FORECASTING; FORESTRY; IMMUNOLOGY; LABORATORIES; PATHOLOGY; VIRUSES;

LEARNING SYSTEMS;

ALGORITHMS; DECISION MAKING; DIAGNOSIS; FORESTRY; LABORATORIES; PATHOLOGY; VIRUSES;

HEPATITIS B VIRUS; HEPATITIS C VIRUS;

ARTICLE; ARTIFICIAL INTELLIGENCE; DECISION SUPPORT SYSTEM; DECISION TREE; HEPATITIS B; HEPATITIS B VIRUS; HEPATITIS C; HEPATITIS C VIRUS; HUMAN; IMMUNOASSAY; IMMUNOLOGICAL PROCEDURES; ISOLATION AND PURIFICATION; VIROLOGY; HEPACIVIRUS;

ARTIFICIAL INTELLIGENCE; DECISION SUPPORT TECHNIQUES; DECISION TREES; HEPACIVIRUS; HEPATITIS B; HEPATITIS B VIRUS; HEPATITIS C; HUMANS; IMMUNOASSAY; IMMUNOLOGIC TESTS;

EID: 84879196286     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-14-206     Document Type: Article

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