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Application of a fuzzy decision tree with ambiguity classification to determine excess weight in schoolchildren [Aplicación de un árbol de decisión difusa con clasificación de ambigüedad para determinar el exceso de peso en escolares]

dc.contributor.authorSulla-Torres J.
dc.contributor.authorGómez-Campos R.
dc.contributor.authorCossio-Bolaños M.A.
dc.date.accessioned2020-09-02T22:28:48Z
dc.date.available2020-09-02T22:28:48Z
dc.date.issued2018
dc.identifier10.17488/RMIB.39.2.1
dc.identifier.citation39, 2, 128-143
dc.identifier.issn01889532
dc.identifier.urihttps://hdl.handle.net/20.500.12728/6352
dc.descriptionThe decision tree technique in the health sciences serves to understand the correlations between the descriptions of patients and to classify accurately in various categories. The aim of the study was to analyze the accuracy of the classification of excess weight of schoolchildren through the application of a fuzzy decision tree, using a database of Itaupú, Paraná (Brazil). We used the database of a sample consisting of 5962 students (3024 female and 2938 male), with an age range between 6 to 17 years of age. The variables considered were weight, height and the Body Mass Index (BMI). To classify the anthropometric data of the students, a diffuse decision tree was used. The learning results showed a correct classification in the female sex of 2688 and in the male sex of 2471 records respectively. In relation to accuracy, 84% was determined in the male sex and 89% in the female sex. The Area under the curve showed higher values in the Fuzzy method and in both sexes (0.965-0.983), while in the classical method, they were lower (0.804-0.895). According to the calculated results it is possible to apply the fuzzy decision tree for the classification of overweight students with an acceptable accuracy, and it is presented as an alternative technique that can save time when analyzing the nutritional status, however, no other statistical calculations were made that have to do with the precision and accuracy through conventional statistical methods and compare with the technique of fuzzy trees. © 2018 Sociedad Mexicana de Ingenieria Biomedica. All rights reserved.
dc.language.isoes
dc.publisherSociedad Mexicana de Ingenieria Biomedica
dc.subjectAmbiguity
dc.subjectClassification obesity
dc.subjectFuzzy decision trees
dc.subjectVagueness
dc.titleApplication of a fuzzy decision tree with ambiguity classification to determine excess weight in schoolchildren [Aplicación de un árbol de decisión difusa con clasificación de ambigüedad para determinar el exceso de peso en escolares]
dc.typeArticle


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