ArticleJournal of medical Internet research2021
Classification Accuracy of Hepatitis C Virus Infection Outcome: Data Mining Approach.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Detecting diseases in medical prescriptions using data mining methods.BioData mining · 2022Article
- Classification Accuracy of Hepatitis C Virus Infection Outcome: Data Mining Approach.Journal of medical Internet research · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
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