RT Journal Article
T1 Classification Accuracy of Hepatitis C Virus Infection Outcome: Data Mining Approach.
A1 Frias, Mario
A1 Moyano, Jose M
A1 Rivero-Juarez, Antonio
A1 Luna, Jose M
A1 Camacho, Angela
A1 Fardoun, Habib M
A1 Machuca, Isabel
A1 Al-Twijri, Mohamed
A1 Rivero, Antonio
A1 Ventura, Sebastian
K1 HIV/HCV
K1 PART
K1 Classification accuracy
K1 Data mining
K1 Ensemble
AB The dataset from genes used to predict hepatitis C virus outcome was evaluated in a previous study using a conventional statistical methodology. 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. 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. 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. Data mining can be used more extensively in biomedicine, facilitating knowledge building and management of human diseases.
PB JMIR Publications
YR 2020
FD 2020-12-17
LK http://hdl.handle.net/10668/17227
UL http://hdl.handle.net/10668/17227
LA en
NO Frias M, Moyano JM, Rivero-Juarez A, Luna JM, Camacho Á, Fardoun HM, et al.  Classification Accuracy of Hepatitis C Virus Infection Outcome: Data Mining Approach. J Med Internet Res. 2021 Feb 24;23(2):e18766
NO This work was supported by the Fundación Progreso y Salud, Consejería de Salud de la Junta de Andalucía (PI0187/2013), the Fundación para la Investigación en Salud del Instituto Carlos III (PI15/01017), the Red de Investigación en SIDA de España ISCIII-RETIC (RD16/0025/0034), the University of Cordoba and Andalusian Council of Economy, Knowledge, Business, and Universities (project 1262678-F), and the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (TIN2017-83445-P). AR is the beneficiary of Contratos para la intensificación de la actividad investigadora en el Sistema Nacional de Salud by the Ministerio de Ciencia, Promoción y Universidades of Spain (INT20-00028). ARJ and MF are recipient of postdoctoral perfection grants by the Ministerio de Ciencia, Innovación y Universidades of Spain (CP18/00111and CD18/00091, respectively). We would like to thank Mrs Janet Dawson for revising the final manuscript.
DS RISalud
RD Apr 7, 2025