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dc.creatorSilva, Jesús
dc.creatorPineda Lezama, Omar Bonerge
dc.creatorVarela, Noel
dc.creatorAdriana Borrero, Luz
dc.date.accessioned2019-08-08T14:49:05Z
dc.date.available2019-08-08T14:49:05Z
dc.date.issued2019-04-27
dc.identifier.isbn978-3-030-19222-8
dc.identifier.isbn978-3-030-19223-5
dc.identifier.urihttp://hdl.handle.net/11323/5134
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.es_ES
dc.language.isoenges_ES
dc.publisherInternational Conference on Green, Pervasive, and Cloud Computinges_ES
dc.relation.ispartofhttps://doi.org/10.1007/978-3-030-19223-5_2es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectBreast canceres_ES
dc.subjectRecurrence eventses_ES
dc.subjectNonrecurrence eventses_ES
dc.subjectK-means clusteringes_ES
dc.titleIntegration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrencees_ES
dc.typePreprintes_ES
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