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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45E96QL
Repositorysid.inpe.br/sibgrapi/2021/09.14.17.45
Last Update2021:10.06.02.15.04 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.14.17.45.23
Metadata Last Update2022:09.10.00.16.17 (UTC) administrator
Citation KeyLopesOliBriOliRap:2021:EsNíOb
TitleEstimativa dos Níveis de Obesidade com Base em Hábitos Alimentares e Condição Física Através de Técnicas de Aprendizado de Máquina
FormatOn-line
Year2021
Access Date2024, Apr. 26
Number of Files1
Size150 KiB
2. Context
Author1 Lopes, Leonardo Ferreira
2 Oliveira, Adonias Caetano de
3 Brito, Rhyan Ximenes de
4 Oliveira, Saulo Anderson Freitas de
5 Raposo Neto, Luiz Torres
Affiliation1 IFCE
2 IFCE
3 IFCE
4 IFCE
5 IFCE
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
e-Mail Addressadonias.oliveira@ifce.edu.br
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeWork in Progress
History (UTC)2021-10-06 02:15:04 :: adonias.oliveira@ifce.edu.br -> administrator :: 2021
2022-09-10 00:16:17 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsObesidade
Máquinas de Vetores de Suporte
Floresta Aleatória
AbstractObesity is a chronic disease that affects several countries, causing damage such as respiratory and locomotor difficulties, metabolic changes, cardiovascular problems, and even death, in the extreme case. In this perspective, this initial study aims to evaluate the classifiers' performance, namely, Random Forest and Support Vector Machine, when estimating obesity levels, with data from the set 'Estimation of obesity levels based on eating habits and physical condition Data Set'. Under cross-validation and Hold-Out, preliminary results indicate an average accuracy with SVM around 87.84% and RF around 95.18%. Furthermore, we noticed that our approach recognizes overweight and obesity cases better, while such cases, in the latest work, are more critically neglected, misclassifying the most severe degree of obesity. Thus, comparing our results with related works, we concluded that the models studied are suitable to the problem, given the achieved results.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2021 > Estimativa dos Níveis...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45E96QL
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45E96QL
Languagept
Target Fileversao_final.pdf
User Groupadonias.oliveira@ifce.edu.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 4
sid.inpe.br/banon/2001/03.30.15.38.24 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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