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Tytuł:
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Classification Accuracy of Hepatitis C Virus Infection Outcome: Data Mining Approach
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Autorzy:
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Frias, Mario
Moyano, Jose M
Rivero-Juarez, Antonio
Luna, Jose M
Camacho, Ángela
Fardoun, Habib M
Machuca, Isabel
Al-Twijri, Mohamed
Rivero, Antonio
Ventura, Sebastian
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Temat:
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Computer applications to medicine. Medical informatics
R858-859.7
Public aspects of medicine
RA1-1270
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Źródło:
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Journal of Medical Internet Research, Vol 23, Iss 2, p e18766 (2021)
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Wydawca:
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JMIR Publications, 2021.
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Rok publikacji:
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2021
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Kolekcja:
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LCC:Computer applications to medicine. Medical informatics
LCC:Public aspects of medicine
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Typ dokumentu:
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article
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Opis pliku:
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electronic resource
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Język:
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English
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ISSN:
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1438-8871
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Relacje:
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https://www.jmir.org/2021/2/e18766; https://doaj.org/toc/1438-8871
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DOI:
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10.2196/18766
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Dostęp URL:
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https://doaj.org/article/0572ff0a790e48d7b5f6847c6351acf2  Link otwiera się w nowym oknie
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Numer akcesji:
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edsdoj.0572ff0a790e48d7b5f6847c6351acf2
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BackgroundThe dataset from genes used to predict hepatitis C virus outcome was evaluated in a previous study using a conventional statistical methodology. ObjectiveThe aim of this study was to reanalyze this same dataset using the data mining approach in order to find models that improve the classification accuracy of the genes studied. MethodsWe built predictive models using different subsets of factors, selected according to their importance in predicting patient classification. We then evaluated each independent model and also a combination of them, leading to a better predictive model. ResultsOur data mining approach identified genetic patterns that escaped detection using conventional statistics. More specifically, the partial decision trees and ensemble models increased the classification accuracy of hepatitis C virus outcome compared with conventional methods. ConclusionsData mining can be used more extensively in biomedicine, facilitating knowledge building and management of human diseases.
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