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Detalhes Referência

Tipo
Artigos em Revista

Tipo de Documento
Artigo Completo

Título
Comparative study on the performance of different classification algorithms, combined with pre- and post-processing techniques to handle imbalanced data, in the diagnosis of adult patients with familial hypercholesterolemia

Participantes na publicação
João Albuquerque (Author)
Ana Margarida Medeiros (Author)
Ana Catarina Alves (Author)
Mafalda Bourbon (Author)
Marília Antunes (Author)
Dep. Estatística e Investigação Operacional
CEAUL

Data de Publicação
2022-06-24

Suporte
PLOS ONE

Identificadores da Publicação
ISSN - 1932-6203

Editora
Public Library of Science (PLoS)

Volume
17
Fascículo
6

Página Inicial
e0269713

Identificadores do Documento
DOI - https://doi.org/10.1371/journal.pone.0269713
URL - http://dx.doi.org/10.1371/journal.pone.0269713


Exportar referência

APA
João Albuquerque, Ana Margarida Medeiros, Ana Catarina Alves, Mafalda Bourbon, Marília Antunes, (2022). Comparative study on the performance of different classification algorithms, combined with pre- and post-processing techniques to handle imbalanced data, in the diagnosis of adult patients with familial hypercholesterolemia. PLOS ONE, 17, ISSN 1932-6203. eISSN . http://dx.doi.org/10.1371/journal.pone.0269713

IEEE
João Albuquerque, Ana Margarida Medeiros, Ana Catarina Alves, Mafalda Bourbon, Marília Antunes, "Comparative study on the performance of different classification algorithms, combined with pre- and post-processing techniques to handle imbalanced data, in the diagnosis of adult patients with familial hypercholesterolemia" in PLOS ONE, vol. 17, 2022. 10.1371/journal.pone.0269713

BIBTEX
@article{57497, author = {João Albuquerque and Ana Margarida Medeiros and Ana Catarina Alves and Mafalda Bourbon and Marília Antunes}, title = {Comparative study on the performance of different classification algorithms, combined with pre- and post-processing techniques to handle imbalanced data, in the diagnosis of adult patients with familial hypercholesterolemia}, journal = {PLOS ONE}, year = 2022, volume = 17 }