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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
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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| Hauptverfasser: | , |
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| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
BioMed Central
2013
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| Schlagworte: | |
| Online Zugang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3697984/ https://ncbi.nlm.nih.gov/pubmed/23800244 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/1471-2105-14-206 |
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