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dc.contributor.authorCardoso, Lucas Buk
dc.contributor.authorParro, Vanderlei Cunha
dc.contributor.authorPeres, Stela Verzinhasse
dc.contributor.authorCurado, Maria Paula
dc.contributor.authorFernandes, Gisele Aparecida
dc.contributor.authorWünsch Filho, Victor
dc.contributor.authorToporcov, Tatiana Natasha
dc.date.accessioned2024-10-15T21:37:31Z
dc.date.available2024-10-15T21:37:31Z
dc.date.issued2023
dc.identifier.issn2045-2322
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85160934912&doi=10.1038%2fs41598-023-35649-9&partnerID=40&md5=84d9a21a1b7818eb7eadd696b171bb23
dc.identifier.urihttps://repositorio.maua.br/handle/MAUA/1434
dc.description.abstractColorectal cancer is one of the most incident types of cancer in the world, with almost 2 million new cases annually. In Brazil, the scenery is the same, around 41 thousand new cases were estimated in the last 3 years. This increase in cases further intensifies the interest and importance of studies related to the topic, especially using new approaches. The use of machine learning algorithms for cancer studies has grown in recent years, and they can provide important information to medicine, in addition to making predictions based on the data. In this study, five different classifications were performed, considering patients’ survival. Data were extracted from Hospital Based Cancer Registries of São Paulo, which is coordinated by Fundação Oncocentro de São Paulo, containing patients with colorectal cancer from São Paulo state, Brazil, treated between 2000 and 2021. The machine learning models used provided us the predictions and the most important features for each one of the algorithms of the studies. Using part of the dataset to validate our models, the results of the predictors were around 77% of accuracy, with AUC close to 0.86, and the most important column was the clinical staging in all of them. © 2023, The Author(s).en
dc.languageInglêspt_BR
dc.publisherNature Researchen
dc.relation.ispartofScientific Reports
dc.rightsAcesso Aberto
dc.sourceScopusen
dc.subjectBrazilen
dc.subjectColorectal Neoplasmsen
dc.subjectHumansen
dc.subjectIncidenceen
dc.subjectMachine Learningen
dc.subjectRegistriesen
dc.subjectBrazilen
dc.subjectcolorectal tumoren
dc.subjecthumanen
dc.subjectincidenceen
dc.subjectmachine learningen
dc.subjectregisteren
dc.titleMachine learning for predicting survival of colorectal cancer patientsen
dc.typeArtigo de Periódicopt_BR
dc.identifier.doi10.1038/s41598-023-35649-9
dc.description.affiliationNúcleo de Sistemas Eletrônicos Embarcados, Instituto Mauá de Tecnologia, São Paulo, 09580-900, Brazil
dc.description.affiliationInformation and Epidemiology, Fundação Oncocentro de São Paulo, São Paulo, 05409-012, Brazil
dc.description.affiliationEpidemiology and Statistics on Cancer Group, A.C. Camargo Cancer Center, São Paulo, 01525-001, Brazil
dc.description.affiliationEpidemiology Department, Faculdade de Saude Pública da Universidade de São Paulo, São Paulo, 01246-904, Brazil
dc.identifier.scopus2-s2.0-85160934912
dc.citation.issue1
dc.citation.volume13


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