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Data mining approach improves classification accuracy of HCV infection outcome
Mario Frias;
Jose M. Moyano;
Antonio Rivero-Juarez;
Jose M. Luna;
Ángela Camacho;
Habib M. Fardoun;
Isabel Machuca;
Mohamed Al-Twijri;
Antonio Rivero;
Sebastian Ventura
ABSTRACT
Background:
The dataset from genes used for the prediction of HCV outcome was evaluated in a previous study by means of conventional statistical methodology.
Objective:
The aim of this study was reanalyze this same dataset using the data mining approach in order to find models that improve the classification accuracy of the genes studied.
Methods:
We built predictive models using different subsets of markers, which were selected according to their importance in predicting the patient classification. Then, we evaluate not only each independent model but also a combination of them, leading to a better predictive model.
Results:
Performance of data mining identified genetic patterns that were hidden by the conventional methodology. Specifically, a PART and ENSEMBLE models, increased the classification accuracy of HCV outcome compared with conventional methodology.
Conclusions:
Data mining can be used more extensively in biomedicine, facilitating knowledge and management of human diseases.
Citation
Please cite as:
Frias M, Moyano JM, Rivero-Juarez A, Luna JM, Camacho , Fardoun HM, Machuca I, Al-Twijri M, Rivero A, Ventura S
Classification Accuracy of Hepatitis C Virus Infection Outcome: Data Mining Approach