Application of data mining for the prediction of mortality and occurrence of complications for gastric cancer patients
Autor(a) principal: | |
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Data de Publicação: | 2019 |
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/1822/63310 |
Resumo: | The development of malign cells that can grow in any part of the stomach, known as gastric cancer, is one of the most common causes of death worldwide. In order to increase the survival rate in patients with this condition, it is essential to improve the decision-making process leading to a better and more efficient selection of treatment strategies. Nowadays, with the large amount of information present in hospital institutions, it is possible to use data mining algorithms to improve the healthcare delivery. Thus, this study, using the CRISP methodology, aims to predict not only the mortality associated with this disease, but also the occurrence of any complication following surgery. A set of classification models were tested and compared in order to improve the prediction accuracy. The study showed that, on one hand, the J48 algorithm using oversampling is the best technique to predict the mortality in gastric cancer patients, with an accuracy of approximately 74%. On the other hand, the rain forest algorithm using oversampling presents the best results when predicting the possible occurrence of complications among gastric cancer patients after their in-hospital stays, with an accuracy of approximately 83%. |
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Application of data mining for the prediction of mortality and occurrence of complications for gastric cancer patientshealthcaregastric cancerknowledge discovery in databasesdata miningclassificationpredictionclinical decision support systemsCRISP-DMWEKAScience & TechnologyThe development of malign cells that can grow in any part of the stomach, known as gastric cancer, is one of the most common causes of death worldwide. In order to increase the survival rate in patients with this condition, it is essential to improve the decision-making process leading to a better and more efficient selection of treatment strategies. Nowadays, with the large amount of information present in hospital institutions, it is possible to use data mining algorithms to improve the healthcare delivery. Thus, this study, using the CRISP methodology, aims to predict not only the mortality associated with this disease, but also the occurrence of any complication following surgery. A set of classification models were tested and compared in order to improve the prediction accuracy. The study showed that, on one hand, the J48 algorithm using oversampling is the best technique to predict the mortality in gastric cancer patients, with an accuracy of approximately 74%. On the other hand, the rain forest algorithm using oversampling presents the best results when predicting the possible occurrence of complications among gastric cancer patients after their in-hospital stays, with an accuracy of approximately 83%.This research was funded by FCT—Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019.Multidisciplinary Digital Publishing InstituteUniversidade do MinhoNeto, CristianaBrito, MariaLopes, VítorPeixoto, HugoAbelha, AntónioMachado, José Manuel20192019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/63310engNeto, C.; Brito, M.; Lopes, V.; Peixoto, H.; Abelha, A.; Machado, J. Application of Data Mining for the Prediction of Mortality and Occurrence of Complications for Gastric Cancer Patients. Entropy 2019, 21, 1163.1099-430010.3390/e21121163https://www.mdpi.com/1099-4300/21/12/1163info: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:RCAAP2023-07-21T12:31:55Zoai:repositorium.sdum.uminho.pt:1822/63310Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:27:13.557566Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Application of data mining for the prediction of mortality and occurrence of complications for gastric cancer patients |
title |
Application of data mining for the prediction of mortality and occurrence of complications for gastric cancer patients |
spellingShingle |
Application of data mining for the prediction of mortality and occurrence of complications for gastric cancer patients Neto, Cristiana healthcare gastric cancer knowledge discovery in databases data mining classification prediction clinical decision support systems CRISP-DM WEKA Science & Technology |
title_short |
Application of data mining for the prediction of mortality and occurrence of complications for gastric cancer patients |
title_full |
Application of data mining for the prediction of mortality and occurrence of complications for gastric cancer patients |
title_fullStr |
Application of data mining for the prediction of mortality and occurrence of complications for gastric cancer patients |
title_full_unstemmed |
Application of data mining for the prediction of mortality and occurrence of complications for gastric cancer patients |
title_sort |
Application of data mining for the prediction of mortality and occurrence of complications for gastric cancer patients |
author |
Neto, Cristiana |
author_facet |
Neto, Cristiana Brito, Maria Lopes, Vítor Peixoto, Hugo Abelha, António Machado, José Manuel |
author_role |
author |
author2 |
Brito, Maria Lopes, Vítor Peixoto, Hugo Abelha, António Machado, José Manuel |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Neto, Cristiana Brito, Maria Lopes, Vítor Peixoto, Hugo Abelha, António Machado, José Manuel |
dc.subject.por.fl_str_mv |
healthcare gastric cancer knowledge discovery in databases data mining classification prediction clinical decision support systems CRISP-DM WEKA Science & Technology |
topic |
healthcare gastric cancer knowledge discovery in databases data mining classification prediction clinical decision support systems CRISP-DM WEKA Science & Technology |
description |
The development of malign cells that can grow in any part of the stomach, known as gastric cancer, is one of the most common causes of death worldwide. In order to increase the survival rate in patients with this condition, it is essential to improve the decision-making process leading to a better and more efficient selection of treatment strategies. Nowadays, with the large amount of information present in hospital institutions, it is possible to use data mining algorithms to improve the healthcare delivery. Thus, this study, using the CRISP methodology, aims to predict not only the mortality associated with this disease, but also the occurrence of any complication following surgery. A set of classification models were tested and compared in order to improve the prediction accuracy. The study showed that, on one hand, the J48 algorithm using oversampling is the best technique to predict the mortality in gastric cancer patients, with an accuracy of approximately 74%. On the other hand, the rain forest algorithm using oversampling presents the best results when predicting the possible occurrence of complications among gastric cancer patients after their in-hospital stays, with an accuracy of approximately 83%. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019 2019-01-01T00:00:00Z |
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/1822/63310 |
url |
http://hdl.handle.net/1822/63310 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Neto, C.; Brito, M.; Lopes, V.; Peixoto, H.; Abelha, A.; Machado, J. Application of Data Mining for the Prediction of Mortality and Occurrence of Complications for Gastric Cancer Patients. Entropy 2019, 21, 1163. 1099-4300 10.3390/e21121163 https://www.mdpi.com/1099-4300/21/12/1163 |
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 |
Multidisciplinary Digital Publishing Institute |
publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute |
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 |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132762492370944 |