Performance comparison of different classification algorithms applied to the diagnosis of familial hypercholesterolemia in paediatric subjects
Autor(a) principal: | |
---|---|
Data de Publicação: | 2022 |
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/10400.18/8288 |
Resumo: | Observational Study |
id |
RCAP_34a5c7d7519fbaab54b96ce0bf58caae |
---|---|
oai_identifier_str |
oai:repositorio.insa.pt:10400.18/8288 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Performance comparison of different classification algorithms applied to the diagnosis of familial hypercholesterolemia in paediatric subjectsFamilial HypercholesterolemiaCholesterolDoenças Cardio e Cérebro-vascularesColesterolHipercolesterolemia FamiliarObservational StudyFamilial Hypercholesterolemia (FH) is an inherited disorder of lipid metabolism, characterized by increased low density lipoprotein cholesterol (LDLc) levels. The main purpose of the current work was to explore alternative classification methods to traditional clinical criteria for FH diagnosis, based on several biochemical and biological indicators. Logistic regression (LR), decision tree (DT), random forest (RF) and naive Bayes (NB) algorithms were developed for this purpose, and thresholds were optimized by maximization of Youden index (YI). All models presented similar accuracy (Acc), specificity (Spec) and positive predictive values (PPV). Sensitivity (Sens) and G-mean values were significantly higher in LR and RF models, compared to the DT. When compared to Simon Broome (SB) biochemical criteria for FH diagnosis, all models presented significantly higher Acc, Spec and G-mean values (p < 0.01), and lower negative predictive value (NPV, p < 0.05). Moreover, LR and RF models presented comparable Sens values. Adjustment of the cut-off point by maximizing YI significantly increased Sens values, with no significant loss in Acc. The obtained results suggest such classification algorithms can be a viable alternative to be used as a widespread screening method. An online application has been developed to assess the performance of the LR model in a wider population.NORTE-08-5369-FSE-000018/Horizon 2020 Framework Programme UID/MAT/00006/2019/Fundação para a Ciência e a Tecnologia PTDC/SAU-SER/29180/2017/Fundação para a Ciência e a TecnologiaNature ResearchRepositório Científico do Instituto Nacional de SaúdeAlbuquerque, JoãoMedeiros, Ana MargaridaAlves, Ana CatarinaBourbon, MafaldaAntunes, Marília2022-10-31T14:47:46Z2022-01-212022-01-21T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.18/8288engSci Rep. 2022 Jan 21;12(1):1164. doi: 10.1038/s41598-022-05063-82045-232210.1038/s41598-022-05063-8info: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:RCAAP2023-07-20T15:42:27Zoai:repositorio.insa.pt:10400.18/8288Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:42:53.314735Repositório 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 |
Performance comparison of different classification algorithms applied to the diagnosis of familial hypercholesterolemia in paediatric subjects |
title |
Performance comparison of different classification algorithms applied to the diagnosis of familial hypercholesterolemia in paediatric subjects |
spellingShingle |
Performance comparison of different classification algorithms applied to the diagnosis of familial hypercholesterolemia in paediatric subjects Albuquerque, João Familial Hypercholesterolemia Cholesterol Doenças Cardio e Cérebro-vasculares Colesterol Hipercolesterolemia Familiar |
title_short |
Performance comparison of different classification algorithms applied to the diagnosis of familial hypercholesterolemia in paediatric subjects |
title_full |
Performance comparison of different classification algorithms applied to the diagnosis of familial hypercholesterolemia in paediatric subjects |
title_fullStr |
Performance comparison of different classification algorithms applied to the diagnosis of familial hypercholesterolemia in paediatric subjects |
title_full_unstemmed |
Performance comparison of different classification algorithms applied to the diagnosis of familial hypercholesterolemia in paediatric subjects |
title_sort |
Performance comparison of different classification algorithms applied to the diagnosis of familial hypercholesterolemia in paediatric subjects |
author |
Albuquerque, João |
author_facet |
Albuquerque, João Medeiros, Ana Margarida Alves, Ana Catarina Bourbon, Mafalda Antunes, Marília |
author_role |
author |
author2 |
Medeiros, Ana Margarida Alves, Ana Catarina Bourbon, Mafalda Antunes, Marília |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Nacional de Saúde |
dc.contributor.author.fl_str_mv |
Albuquerque, João Medeiros, Ana Margarida Alves, Ana Catarina Bourbon, Mafalda Antunes, Marília |
dc.subject.por.fl_str_mv |
Familial Hypercholesterolemia Cholesterol Doenças Cardio e Cérebro-vasculares Colesterol Hipercolesterolemia Familiar |
topic |
Familial Hypercholesterolemia Cholesterol Doenças Cardio e Cérebro-vasculares Colesterol Hipercolesterolemia Familiar |
description |
Observational Study |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-10-31T14:47:46Z 2022-01-21 2022-01-21T00:00:00Z |
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/10400.18/8288 |
url |
http://hdl.handle.net/10400.18/8288 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Sci Rep. 2022 Jan 21;12(1):1164. doi: 10.1038/s41598-022-05063-8 2045-2322 10.1038/s41598-022-05063-8 |
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 |
Nature Research |
publisher.none.fl_str_mv |
Nature Research |
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 |
|
_version_ |
1799132174778105856 |