Treating colon cancer survivability prediction as a classification problem
Autor(a) principal: | |
---|---|
Data de Publicação: | 2016 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/50532 |
Resumo: | This work presents a survivability prediction model for colon cancer developed with machine learning techniques. Survivability was viewed as a classification task where it was necessary to determine if a patient would survive each of the five years following treatment. The model was based on the SEER dataset which, after preprocessing, consisted of 38,592 records of colon cancer patients. Six features were extracted from a feature selection process in order to construct the model. This model was compared with another one with 18 features indicated by a physician. The results show that the performance of the sixfeature model is close to that of the model using 18 features, which indicates that the first may be a good compromise between usability and performance. |
id |
RCAP_64418e81ecad0257765bdb773b950bfe |
---|---|
oai_identifier_str |
oai:repositorium.sdum.uminho.pt:1822/50532 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
|
spelling |
Treating colon cancer survivability prediction as a classification problemColon cancerPredictionMachine learningEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaScience & TechnologyThis work presents a survivability prediction model for colon cancer developed with machine learning techniques. Survivability was viewed as a classification task where it was necessary to determine if a patient would survive each of the five years following treatment. The model was based on the SEER dataset which, after preprocessing, consisted of 38,592 records of colon cancer patients. Six features were extracted from a feature selection process in order to construct the model. This model was compared with another one with 18 features indicated by a physician. The results show that the performance of the sixfeature model is close to that of the model using 18 features, which indicates that the first may be a good compromise between usability and performance.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013. The work of Tiago Oliveira is supported by a FCT grant with the reference SFRH/BD/85291/ 2012.info:eu-repo/semantics/publishedVersionEdiciones Universidad de SalamancaUniversidade do MinhoSilva, Ana Paula Pinto daOliveira, Tiago José MartinsNeves, JoséNovais, Paulo20162016-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/50532eng2255-286310.14201/ADCAIJ2016513750info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:08:31ZPortal AgregadorONG |
dc.title.none.fl_str_mv |
Treating colon cancer survivability prediction as a classification problem |
title |
Treating colon cancer survivability prediction as a classification problem |
spellingShingle |
Treating colon cancer survivability prediction as a classification problem Silva, Ana Paula Pinto da Colon cancer Prediction Machine learning Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática Science & Technology |
title_short |
Treating colon cancer survivability prediction as a classification problem |
title_full |
Treating colon cancer survivability prediction as a classification problem |
title_fullStr |
Treating colon cancer survivability prediction as a classification problem |
title_full_unstemmed |
Treating colon cancer survivability prediction as a classification problem |
title_sort |
Treating colon cancer survivability prediction as a classification problem |
author |
Silva, Ana Paula Pinto da |
author_facet |
Silva, Ana Paula Pinto da Oliveira, Tiago José Martins Neves, José Novais, Paulo |
author_role |
author |
author2 |
Oliveira, Tiago José Martins Neves, José Novais, Paulo |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Silva, Ana Paula Pinto da Oliveira, Tiago José Martins Neves, José Novais, Paulo |
dc.subject.por.fl_str_mv |
Colon cancer Prediction Machine learning Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática Science & Technology |
topic |
Colon cancer Prediction Machine learning Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática Science & Technology |
description |
This work presents a survivability prediction model for colon cancer developed with machine learning techniques. Survivability was viewed as a classification task where it was necessary to determine if a patient would survive each of the five years following treatment. The model was based on the SEER dataset which, after preprocessing, consisted of 38,592 records of colon cancer patients. Six features were extracted from a feature selection process in order to construct the model. This model was compared with another one with 18 features indicated by a physician. The results show that the performance of the sixfeature model is close to that of the model using 18 features, which indicates that the first may be a good compromise between usability and performance. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016 2016-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/50532 |
url |
http://hdl.handle.net/1822/50532 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2255-2863 10.14201/ADCAIJ2016513750 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Ediciones Universidad de Salamanca |
publisher.none.fl_str_mv |
Ediciones Universidad de Salamanca |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
|
repository.mail.fl_str_mv |
|
_version_ |
1777303690304028672 |