A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis

Detalhes bibliográficos
Autor(a) principal: Brito, Claúdia
Data de Publicação: 2022
Outros Autores: Esteves, Marisa, Peixoto, Hugo, 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/1822/89567
Resumo: Continuous ambulatory peritoneal dialysis (CAPD) is a treatment used by patients in the end-stage of chronic kidney diseases. Those patients need to be monitored using blood tests and those tests can present some patterns or correlations. It could be meaningful to apply data mining (DM) to the data collected from those tests. To discover patterns from meaningless data, it becomes crucial to use DM techniques. DM is an emerging field that is currently being used in machine learning to train machines to later aid health professionals in their decision-making process. The classification process can found patterns useful to understand the patients’ health development and to medically act according to such results. Thus, this study focuses on testing a set of DM algorithms that may help in classifying the values of serum creatinine in patients undergoing CAPD procedures. Therefore, it is intended to classify the values of serum creatinine according to assigned quartiles. The better results obtained were highly satisfactory, reaching accuracy rate values of approximately 95%, and low relative absolute error values.
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spelling A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysisChronic kidney diseasesClassification algorithmsClinical decision support systemsContinuous ambulatory peritoneal dialysisData miningKnowledge extractionSerum creatinineWekaEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaScience & TechnologyContinuous ambulatory peritoneal dialysis (CAPD) is a treatment used by patients in the end-stage of chronic kidney diseases. Those patients need to be monitored using blood tests and those tests can present some patterns or correlations. It could be meaningful to apply data mining (DM) to the data collected from those tests. To discover patterns from meaningless data, it becomes crucial to use DM techniques. DM is an emerging field that is currently being used in machine learning to train machines to later aid health professionals in their decision-making process. The classification process can found patterns useful to understand the patients’ health development and to medically act according to such results. Thus, this study focuses on testing a set of DM algorithms that may help in classifying the values of serum creatinine in patients undergoing CAPD procedures. Therefore, it is intended to classify the values of serum creatinine according to assigned quartiles. The better results obtained were highly satisfactory, reaching accuracy rate values of approximately 95%, and low relative absolute error values.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.SpringerUniversidade do MinhoBrito, ClaúdiaEsteves, MarisaPeixoto, HugoAbelha, AntónioMachado, José Manuel20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/89567engBrito, C., Esteves, M., Peixoto, H. et al. A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis. Wireless Netw 28, 1269–1277 (2022). https://doi.org/10.1007/s11276-018-01905-41022-003810.1007/s11276-018-01905-4https://link.springer.com/article/10.1007/s11276-018-01905-4info: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:RCAAP2024-03-16T01:22:45Zoai:repositorium.sdum.uminho.pt:1822/89567Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T04:01:14.601522Repositório 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 A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis
title A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis
spellingShingle A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis
Brito, Claúdia
Chronic kidney diseases
Classification algorithms
Clinical decision support systems
Continuous ambulatory peritoneal dialysis
Data mining
Knowledge extraction
Serum creatinine
Weka
Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
Science & Technology
title_short A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis
title_full A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis
title_fullStr A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis
title_full_unstemmed A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis
title_sort A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis
author Brito, Claúdia
author_facet Brito, Claúdia
Esteves, Marisa
Peixoto, Hugo
Abelha, António
Machado, José Manuel
author_role author
author2 Esteves, Marisa
Peixoto, Hugo
Abelha, António
Machado, José Manuel
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Brito, Claúdia
Esteves, Marisa
Peixoto, Hugo
Abelha, António
Machado, José Manuel
dc.subject.por.fl_str_mv Chronic kidney diseases
Classification algorithms
Clinical decision support systems
Continuous ambulatory peritoneal dialysis
Data mining
Knowledge extraction
Serum creatinine
Weka
Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
Science & Technology
topic Chronic kidney diseases
Classification algorithms
Clinical decision support systems
Continuous ambulatory peritoneal dialysis
Data mining
Knowledge extraction
Serum creatinine
Weka
Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
Science & Technology
description Continuous ambulatory peritoneal dialysis (CAPD) is a treatment used by patients in the end-stage of chronic kidney diseases. Those patients need to be monitored using blood tests and those tests can present some patterns or correlations. It could be meaningful to apply data mining (DM) to the data collected from those tests. To discover patterns from meaningless data, it becomes crucial to use DM techniques. DM is an emerging field that is currently being used in machine learning to train machines to later aid health professionals in their decision-making process. The classification process can found patterns useful to understand the patients’ health development and to medically act according to such results. Thus, this study focuses on testing a set of DM algorithms that may help in classifying the values of serum creatinine in patients undergoing CAPD procedures. Therefore, it is intended to classify the values of serum creatinine according to assigned quartiles. The better results obtained were highly satisfactory, reaching accuracy rate values of approximately 95%, and low relative absolute error values.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-01T00:00:00Z
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 https://hdl.handle.net/1822/89567
url https://hdl.handle.net/1822/89567
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Brito, C., Esteves, M., Peixoto, H. et al. A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis. Wireless Netw 28, 1269–1277 (2022). https://doi.org/10.1007/s11276-018-01905-4
1022-0038
10.1007/s11276-018-01905-4
https://link.springer.com/article/10.1007/s11276-018-01905-4
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 Springer
publisher.none.fl_str_mv Springer
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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