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dc.contributor.authorSilva, Jesússpa
dc.contributor.authorPineda Lezama, Omar Bonergespa
dc.contributor.authorVarela, Noelspa
dc.contributor.authorAdriana Borrero, Luzspa
dc.date.accessioned2019-08-08T14:49:05Z
dc.date.available2019-08-08T14:49:05Z
dc.date.issued2019-04-27
dc.identifier.isbn978-3-030-19222-8spa
dc.identifier.isbn978-3-030-19223-5spa
dc.identifier.urihttp://hdl.handle.net/11323/5134spa
dc.description.abstractConservative surgery plus radiotherapy is an alternative to radical mastectomy in the early stages of breast cancer, presenting equivalent survival rates. Data mining facilitates to manage the data and provide the useful medical progression and treatment of cancerous conditions as these methods can help to reduce the number of false positive and false negative decisions. Various machine learning techniques can be used to support the doctors in effective and accurate decision making. In this paper, various classifiers have been tested for the prediction of type of breast cancer recurrence and the results show that neural networks outperform others.spa
dc.language.isoeng
dc.publisherInternational Conference on Green, Pervasive, and Cloud Computingspa
dc.relation.ispartofhttps://doi.org/10.1007/978-3-030-19223-5_2spa
dc.rightsCC0 1.0 Universalspa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
dc.subjectBreast cancerspa
dc.subjectRecurrence eventsspa
dc.subjectNonrecurrence eventsspa
dc.subjectK-means clusteringspa
dc.titleIntegration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrencespa
dc.typePre-Publicaciónspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
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