Obesity level estimation software based on decision trees
Obesity has become a global epidemic that has doubled since 1980, with serious consequences for health in children, teenagers and adults. Obesity is a problem has been growing steadily and that is why every day appear new studies involving children obesity, especially those looking for influence fac...
- Autores:
-
De-La-Hoz-Correa, Eduardo
Mendoza Palechor, Fabio
De-La-Hoz-Manotas, Alexis
Morales Ortega, Roberto
Sánchez Hernández, Adriana Beatriz
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2019
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/4176
- Acceso en línea:
- https://hdl.handle.net/11323/4176
https://repositorio.cuc.edu.co/
- Palabra clave:
- Data mining
Decision trees
Java
Logistic regression
Naive bayes
Obesity
Semma
Weka
Minería de datos
Árboles de decisión
Java
Regresión logística
Bayas ingenuas
Obesidad
Semma
- Rights
- openAccess
- License
- Attribution-NonCommercial-ShareAlike 4.0 International
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| dc.title.spa.fl_str_mv |
Obesity level estimation software based on decision trees |
| dc.title.translated.spa.fl_str_mv |
Software de estimación de nivel de obesidad basado en árboles de decisión. |
| title |
Obesity level estimation software based on decision trees |
| spellingShingle |
Obesity level estimation software based on decision trees Data mining Decision trees Java Logistic regression Naive bayes Obesity Semma Weka Minería de datos Árboles de decisión Java Regresión logística Bayas ingenuas Obesidad Semma |
| title_short |
Obesity level estimation software based on decision trees |
| title_full |
Obesity level estimation software based on decision trees |
| title_fullStr |
Obesity level estimation software based on decision trees |
| title_full_unstemmed |
Obesity level estimation software based on decision trees |
| title_sort |
Obesity level estimation software based on decision trees |
| dc.creator.fl_str_mv |
De-La-Hoz-Correa, Eduardo Mendoza Palechor, Fabio De-La-Hoz-Manotas, Alexis Morales Ortega, Roberto Sánchez Hernández, Adriana Beatriz |
| dc.contributor.author.spa.fl_str_mv |
De-La-Hoz-Correa, Eduardo Mendoza Palechor, Fabio De-La-Hoz-Manotas, Alexis Morales Ortega, Roberto Sánchez Hernández, Adriana Beatriz |
| dc.subject.spa.fl_str_mv |
Data mining Decision trees Java Logistic regression Naive bayes Obesity Semma Weka Minería de datos Árboles de decisión Java Regresión logística Bayas ingenuas Obesidad Semma |
| topic |
Data mining Decision trees Java Logistic regression Naive bayes Obesity Semma Weka Minería de datos Árboles de decisión Java Regresión logística Bayas ingenuas Obesidad Semma |
| description |
Obesity has become a global epidemic that has doubled since 1980, with serious consequences for health in children, teenagers and adults. Obesity is a problem has been growing steadily and that is why every day appear new studies involving children obesity, especially those looking for influence factors and how to predict emergence of the condition under these factors. In this study, authors applied the SEMMA data mining methodology, to select, explore and model the data set and then three methods were selected: Decision trees (J48), Bayesian networks (Naïve Bayes) and Logistic Regression (Simple Logistic), obtaining the best results with J48 based on the metrics: Precision, recall, TP Rate and FP Rate. Finally, a software was built to use and train the selected method, using the Weka library. The results confirmed the Decision Trees technique has the best precision rate (97.4%), improving results of previous studies with similar background. |
| publishDate |
2019 |
| dc.date.accessioned.none.fl_str_mv |
2019-05-16T20:17:15Z |
| dc.date.available.none.fl_str_mv |
2019-05-16T20:17:15Z |
| dc.date.issued.none.fl_str_mv |
2019-04-15 |
| dc.type.spa.fl_str_mv |
Artículo de revista |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.type.content.spa.fl_str_mv |
Text |
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info:eu-repo/semantics/article |
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http://purl.org/redcol/resource_type/ART |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_6501 |
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acceptedVersion |
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15493636 |
| dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/4176 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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15493636 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/4176 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.rights.spa.fl_str_mv |
Attribution-NonCommercial-ShareAlike 4.0 International |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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info:eu-repo/semantics/openAccess |
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http://purl.org/coar/access_right/c_abf2 |
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Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.publisher.spa.fl_str_mv |
Universidad de la Costa |
| institution |
Corporación Universidad de la Costa |
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De-La-Hoz-Correa, EduardoMendoza Palechor, FabioDe-La-Hoz-Manotas, AlexisMorales Ortega, RobertoSánchez Hernández, Adriana Beatriz2019-05-16T20:17:15Z2019-05-16T20:17:15Z2019-04-1515493636https://hdl.handle.net/11323/4176Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Obesity has become a global epidemic that has doubled since 1980, with serious consequences for health in children, teenagers and adults. Obesity is a problem has been growing steadily and that is why every day appear new studies involving children obesity, especially those looking for influence factors and how to predict emergence of the condition under these factors. In this study, authors applied the SEMMA data mining methodology, to select, explore and model the data set and then three methods were selected: Decision trees (J48), Bayesian networks (Naïve Bayes) and Logistic Regression (Simple Logistic), obtaining the best results with J48 based on the metrics: Precision, recall, TP Rate and FP Rate. Finally, a software was built to use and train the selected method, using the Weka library. The results confirmed the Decision Trees technique has the best precision rate (97.4%), improving results of previous studies with similar background.La obesidad se ha convertido en una epidemia mundial que se ha duplicado desde 1980, con graves consecuencias para la salud en niños, adolescentes y adultos. La obesidad es un problema que ha estado creciendo de manera constante y es por eso que cada día aparecen nuevos estudios que involucran a niños obesos, especialmente aquellos que buscan factores de influencia y cómo predecir la aparición de la enfermedad bajo estos factores. En este estudio, los autores aplicaron la metodología de minería de datos SEMMA, para seleccionar, explorar y modelar el conjunto de datos y luego se seleccionaron tres métodos: árboles de decisión (J48), redes bayesianas (Naïve Bayes) y regresión logística (logística simple), obteniendo la Los mejores resultados con J48 se basan en las métricas: precisión, recuperación, TP Rate y FP Rate. Finalmente, se construyó un software para usar y entrenar el método seleccionado, utilizando la biblioteca Weka. Los resultados confirmaron que la técnica de árboles de decisión tiene la mejor tasa de precisión (97.4%), mejorando los resultados de estudios previos con antecedentes similares.De-La-Hoz-Correa, Eduardo-f50d0e8b-2e3b-4e05-816a-bcd89cf4b021-0Mendoza Palechor, Fabio-28dab11c-3464-49e6-bcdf-32623fd0055b-0De-La-Hoz-Manotas, Alexis-8c2e7635-6db0-49a2-bb3b-b7131e3bad0f-0Morales Ortega, Roberto-ec775181-8c64-4b76-a4ac-1444380e3d0b-0Sánchez Hernández, Adriana Beatriz-fe9616ba-d3f7-4b60-8651-a3fcf8a9626b-0engUniversidad de la CostaAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Data miningDecision treesJavaLogistic regressionNaive bayesObesitySemmaWekaMinería de datosÁrboles de decisiónJavaRegresión logísticaBayas ingenuasObesidadSemmaObesity level estimation software based on decision treesSoftware de estimación de nivel de obesidad basado en árboles de decisión.Artículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionPublicationORIGINALOBESITY LEVEL ESTIMATION SOFTWARE BASED ON DECISION TREES.pdfOBESITY LEVEL ESTIMATION SOFTWARE BASED ON DECISION TREES.pdfapplication/pdf6757https://repositorio.cuc.edu.co/bitstreams/5271ca4d-f773-4752-8cee-0785377c276b/downloada2e32fcd33e631ab3ed15020eec2ae09MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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