Predicting the survival of primary biliary cholangitis patients

Detalhes bibliográficos
Autor(a) principal: Ferreira, Diana
Data de Publicação: 2022
Outros Autores: Neto, Cristiana, Lopes, José, Duarte, Júlio Miguel Marques, Abelha, António, Machado, José Manuel
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/80679
Resumo: Data are available in a publicly accessible repository that does not issue DOIs. Publicly available datasets were analysed in this study. These data can be found here: https://www.kaggle.com/jixing475/mayo-clinic-primary-biliary-cirrhosis-data (accessed on 1 July 2022).
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spelling Predicting the survival of primary biliary cholangitis patientsClassificationData miningPredictive modelsPrimary biliary cholangitisScience & TechnologyData are available in a publicly accessible repository that does not issue DOIs. Publicly available datasets were analysed in this study. These data can be found here: https://www.kaggle.com/jixing475/mayo-clinic-primary-biliary-cirrhosis-data (accessed on 1 July 2022).Primary Biliary Cholangitis, which is thought to be caused by a combination of genetic and environmental factors, is a slow-growing chronic autoimmune disease in which the human body’s immune system attacks healthy cells and tissues and gradually destroys the bile ducts in the liver. A reliable diagnosis of this clinical condition, followed by appropriate intervention measures, can slow the damage to the liver and prevent further complications, especially in the early stages. Hence, the focus of this study is to compare different classification Data Mining techniques, using clinical and demographic data, in an attempt to predict whether or not a Primary Biliary Cholangitis patient will survive. Data from 418 patients with Primary Biliary Cholangitis, following the Mayo Clinic’s research between 1974 and 1984, were used to predict patient survival or non-survival using the Cross Industry Standard Process for Data Mining methodology. Different classification techniques were applied during this process, more specifically, Decision Tree, Random Tree, Random Forest, and Naïve Bayes. The model with the best performance used the Random Forest classifier and Split Validation with a ratio of 0.8, yielding values greater than 93% in all evaluation metrics. With further testing, this model may provide benefits in terms of medical decision support.This work is funded by “Fundação para a Ciência e Tecnologia (FCT)” within the R&D Units Project Scope: UIDB/00319/2020.Multidisciplinary Digital Publishing InstituteUniversidade do MinhoFerreira, DianaNeto, CristianaLopes, JoséDuarte, Júlio Miguel MarquesAbelha, AntónioMachado, José Manuel2022-08-112022-08-11T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/80679engFerreira, D.; Neto, C.; Lopes, J.; Duarte, J.; Abelha, A.; Machado, J. Predicting the Survival of Primary Biliary Cholangitis Patients. Appl. Sci. 2022, 12, 8043. https://doi.org/10.3390/app121680432076-341710.3390/app12168043https://www.mdpi.com/2076-3417/12/16/8043info: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:RCAAP2023-07-21T11:58:48Zoai:repositorium.sdum.uminho.pt:1822/80679Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:48:35.231934Repositório 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 Predicting the survival of primary biliary cholangitis patients
title Predicting the survival of primary biliary cholangitis patients
spellingShingle Predicting the survival of primary biliary cholangitis patients
Ferreira, Diana
Classification
Data mining
Predictive models
Primary biliary cholangitis
Science & Technology
title_short Predicting the survival of primary biliary cholangitis patients
title_full Predicting the survival of primary biliary cholangitis patients
title_fullStr Predicting the survival of primary biliary cholangitis patients
title_full_unstemmed Predicting the survival of primary biliary cholangitis patients
title_sort Predicting the survival of primary biliary cholangitis patients
author Ferreira, Diana
author_facet Ferreira, Diana
Neto, Cristiana
Lopes, José
Duarte, Júlio Miguel Marques
Abelha, António
Machado, José Manuel
author_role author
author2 Neto, Cristiana
Lopes, José
Duarte, Júlio Miguel Marques
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 Ferreira, Diana
Neto, Cristiana
Lopes, José
Duarte, Júlio Miguel Marques
Abelha, António
Machado, José Manuel
dc.subject.por.fl_str_mv Classification
Data mining
Predictive models
Primary biliary cholangitis
Science & Technology
topic Classification
Data mining
Predictive models
Primary biliary cholangitis
Science & Technology
description Data are available in a publicly accessible repository that does not issue DOIs. Publicly available datasets were analysed in this study. These data can be found here: https://www.kaggle.com/jixing475/mayo-clinic-primary-biliary-cirrhosis-data (accessed on 1 July 2022).
publishDate 2022
dc.date.none.fl_str_mv 2022-08-11
2022-08-11T00:00:00Z
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 https://hdl.handle.net/1822/80679
url https://hdl.handle.net/1822/80679
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Ferreira, D.; Neto, C.; Lopes, J.; Duarte, J.; Abelha, A.; Machado, J. Predicting the Survival of Primary Biliary Cholangitis Patients. Appl. Sci. 2022, 12, 8043. https://doi.org/10.3390/app12168043
2076-3417
10.3390/app12168043
https://www.mdpi.com/2076-3417/12/16/8043
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
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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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