Show simple item record

dc.contributor.authorGutierrez-Espinoza, Sandy
dc.contributor.authorCabanillas-Carbonell, Michael
dc.date.accessioned2022-03-10T17:55:22Z
dc.date.available2022-03-10T17:55:22Z
dc.date.issued2021-12-30
dc.identifier.citationGutierrez-Espinoza, S., & Cabanillas-Carbonell, M. (2021, November). Machine Learning Analysis for Cervical Cancer Prediction, a Systematic Review of the Literature. In 2021 International Conference on e-Health and Bioengineering (EHB) (pp. 1-6). IEEE.es_PE
dc.identifier.isbn978-1-6654-4000-4
dc.identifier.issn2575-5145
dc.identifier.urihttps://hdl.handle.net/20.500.13067/1754
dc.description.abstractAt present, cervical cancer is still the most complex issue due to the fact that people who suffer from it have a high risk of death. Therefore, it is very important to have an early diagnosis. The present study is a review of the scientific literature, which includes 50 articles from the following databases: ProQuest, IEEE Xplore, PubMed, ScienceDirect, Springer, IopScience and Scopus. Thus, showing that the research that has been developed with machine learning facilitates the control, follow-up and monitoring of the disease. The systematic review shows that the model that had the highest accuracy is Convolutional Neural Network and the most used tool is R Studio, these two factors are determinant in cervical cancer, according to the research conducted with 50 articles, where more research on this topic was recorded is the continent of Asia and specifically in the countries of India and China.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherInstitute of Electrical and Electronics Engineerses_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/es_PE
dc.sourceAUTONOMAes_PE
dc.subjectSystematicses_PE
dc.subjectAsiaes_PE
dc.subjectMachine learninges_PE
dc.subjectSensitivity and specificityes_PE
dc.subjectPredictive modelses_PE
dc.subjectMathematical modelses_PE
dc.subjectConvolutional neural networkses_PE
dc.titleMachine Learning Analysis for Cervical Cancer Prediction, a Systematic Review of the Literaturees_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.journal2021 International Conference on e-Health and Bioengineering (EHB)es_PE
dc.identifier.doihttps://doi.org/10.1109/EHB52898.2021.9657567
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.04es_PE
dc.publisher.countryPEes_PE
dc.relation.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85124563830&doi=10.1109%2fEHB52898.2021.9657567&partnerID=40es_PE
dc.source.beginpage1es_PE
dc.source.endpage6es_PE


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

info:eu-repo/semantics/restrictedAccess
Except where otherwise noted, this item's license is described as info:eu-repo/semantics/restrictedAccess