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- Title
Classification Accuracy of Hepatitis C Virus Infection Outcome: Data Mining Approach.
- Authors
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
- Abstract
<bold>Background: </bold>The dataset from genes used to predict hepatitis C virus outcome was evaluated in a previous study using a conventional statistical methodology.<bold>Objective: </bold>The 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.<bold>Methods: </bold>We 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.<bold>Results: </bold>Our 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.<bold>Conclusions: </bold>Data mining can be used more extensively in biomedicine, facilitating knowledge building and management of human diseases.
- Subjects
HEPATITIS C virus; DATA mining; VIRUS diseases; CONSTRUCTION management; DECISION trees; RESEARCH; RESEARCH methodology; HEPATITIS viruses; MEDICAL cooperation; EVALUATION research; COMPARATIVE studies; ALGORITHMS
- Publication
Journal of Medical Internet Research, 2021, Vol 23, Issue 2, pN.PAG
- ISSN
1439-4456
- Publication type
journal article
- DOI
10.2196/18766