Understanding stroke in dialysis and chronic kidney disease

Bibliographic Details
Main Author: Rodrigues, Mariana
Publication Date: 2017
Other Authors: Peixoto, Hugo Daniel Abreu, Esteves, Marisa Araújo, Machado, José Manuel, Abelha, António
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: https://hdl.handle.net/1822/51699
Summary: "International Workshop on Healthcare Interoperability and Pervasive Intelligent Systems (HiPIS 2017)"
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spelling Understanding stroke in dialysis and chronic kidney diseaseData MiningClassificationDialysisStroke RiskChronic Kidney DiseaseScience & Technology"International Workshop on Healthcare Interoperability and Pervasive Intelligent Systems (HiPIS 2017)"Patients with severe kidney failure need to be carefully monitored. One of the many treatments is called Continuous Ambulatory Peritoneal Dialysis (CAPD). This kind of treatment intends to maintain the blood tests as normal as possible. Data Mining and Machine Learning can take a simple and meaningless blood's test data set and build it into a Decision Support System. Through this article, Machine Learning algorithms will be explored with different Data Mining Models in order to extract knowledge and classify a patient with a stroke risk or not, according to their blood analysis. Peer-review under responsibility of the Conference Program Chairs.This work has been supported by Compete: POCI-01-0145-FEDER-007043 and FCT within the Project Scope UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersionElsevierUniversidade do MinhoRodrigues, MarianaPeixoto, Hugo Daniel AbreuEsteves, Marisa AraújoMachado, José ManuelAbelha, António20172017-01-01T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/1822/51699eng1877-050910.1016/j.procs.2017.08.296info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-04-12T04:05:58Zoai:repositorium.sdum.uminho.pt:1822/51699Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T14:50:50.583068Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Understanding stroke in dialysis and chronic kidney disease
title Understanding stroke in dialysis and chronic kidney disease
spellingShingle Understanding stroke in dialysis and chronic kidney disease
Rodrigues, Mariana
Data Mining
Classification
Dialysis
Stroke Risk
Chronic Kidney Disease
Science & Technology
title_short Understanding stroke in dialysis and chronic kidney disease
title_full Understanding stroke in dialysis and chronic kidney disease
title_fullStr Understanding stroke in dialysis and chronic kidney disease
title_full_unstemmed Understanding stroke in dialysis and chronic kidney disease
title_sort Understanding stroke in dialysis and chronic kidney disease
author Rodrigues, Mariana
author_facet Rodrigues, Mariana
Peixoto, Hugo Daniel Abreu
Esteves, Marisa Araújo
Machado, José Manuel
Abelha, António
author_role author
author2 Peixoto, Hugo Daniel Abreu
Esteves, Marisa Araújo
Machado, José Manuel
Abelha, António
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Rodrigues, Mariana
Peixoto, Hugo Daniel Abreu
Esteves, Marisa Araújo
Machado, José Manuel
Abelha, António
dc.subject.por.fl_str_mv Data Mining
Classification
Dialysis
Stroke Risk
Chronic Kidney Disease
Science & Technology
topic Data Mining
Classification
Dialysis
Stroke Risk
Chronic Kidney Disease
Science & Technology
description "International Workshop on Healthcare Interoperability and Pervasive Intelligent Systems (HiPIS 2017)"
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-01-01T00:00:00Z
dc.type.driver.fl_str_mv conference paper
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/51699
url https://hdl.handle.net/1822/51699
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1877-0509
10.1016/j.procs.2017.08.296
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 Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833594926769635328

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