Machine learning techniques to predict overweight or obesity

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniv Taubate UNITAU-
Autor(es): dc.creatorRodriguez, Elias [UNESP]-
Autor(es): dc.creatorRodriguez, Elen [UNESP]-
Autor(es): dc.creatorNascimento, Luiz [UNESP]-
Autor(es): dc.creatorSilva, Aneirson da [UNESP]-
Autor(es): dc.creatorMarins, Fernando [UNESP]-
Autor(es): dc.creatorShakhovska, N.-
Autor(es): dc.creatorSalazar, A.-
Autor(es): dc.creatorIzonin, I-
Autor(es): dc.creatorCampos, J.-
Data de aceite: dc.date.accessioned2022-08-04T21:58:35Z-
Data de disponibilização: dc.date.available2022-08-04T21:58:35Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/218587-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/218587-
Descrição: dc.descriptionOverweight and obesity are considered a public health problem, as they are related to the risk of various diseases, and also to the risk of increased morbidity and mortality. The main objective of this work was to apply machine learning techniques for the development of a predictive model for the identification of people with obesity or overweight. The model developed was based on data related to the physical condition and eating habits. Furthermore, the machine learning classification algorithms that were tested were: decision tree,support vector machines, k-nearest neighbors, gaussian naive bayes, multilayer perceptron, random forest, gradient boosting, and extreme gradient boosting. Model hyperparameters were tuned to improve accuracy, resulting in that the model with the best performance was a random forest with 78% accuracy, 79% precision, 78% recall, and 78% F1-score. Finally, the potential of using machine learning models to identify people who are overweight or obese was demonstrated. The practical use of the model developed will allow specialists in the health area to use it as an advantage for decision-making.-
Descrição: dc.descriptionCoordination for the Improvement of Higher Education Personnel-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Av Dr Ariberto Pereira Cunha 333, BR-12516410 Guaratingueta, SP, Brazil-
Descrição: dc.descriptionUniv Taubate UNITAU, Av Prof Walter Taumaturgo 739, BR-12030040 Taubate, SP, Brazil-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Av Dr Ariberto Pereira Cunha 333, BR-12516410 Guaratingueta, SP, Brazil-
Descrição: dc.descriptionCoordination for the Improvement of Higher Education Personnel: CAPES -001-
Formato: dc.format190-204-
Idioma: dc.languageen-
Publicador: dc.publisherRwth Aachen-
Relação: dc.relationIddm 2021: Informatics & Data-driven Medicine: Proceedings Of The 4th International Conference On Informatics & Data-driven Medicine (iddm 2021)-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectOverweight and obesity-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectclassification models-
Palavras-chave: dc.subjectbody mass index-
Título: dc.titleMachine learning techniques to predict overweight or obesity-
Aparece nas coleções:Repositório Institucional - Unesp

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