dc.creatorGutierrez-Espinoza, Sandy
dc.creatorCabanillas-Carbonell, Michael
dc.date.accessioned2022-03-10T17:55:22Z
dc.date.accessioned2023-05-30T23:10:37Z
dc.date.available2022-03-10T17:55:22Z
dc.date.available2023-05-30T23:10:37Z
dc.date.created2022-03-10T17:55:22Z
dc.date.issued2021-12-30
dc.identifierGutierrez-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.
dc.identifier978-1-6654-4000-4
dc.identifier2575-5145
dc.identifierhttps://hdl.handle.net/20.500.13067/1754
dc.identifier2021 International Conference on e-Health and Bioengineering (EHB)
dc.identifierhttps://doi.org/10.1109/EHB52898.2021.9657567
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6472720
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.
dc.languageeng
dc.publisherInstitute of Electrical and Electronics Engineers
dc.publisherPE
dc.relationhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85124563830&doi=10.1109%2fEHB52898.2021.9657567&partnerID=40
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceAUTONOMA
dc.source1
dc.source6
dc.subjectSystematics
dc.subjectAsia
dc.subjectMachine learning
dc.subjectSensitivity and specificity
dc.subjectPredictive models
dc.subjectMathematical models
dc.subjectConvolutional neural networks
dc.titleMachine Learning Analysis for Cervical Cancer Prediction, a Systematic Review of the Literature
dc.typeinfo:eu-repo/semantics/article


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