Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Archives of Biology and Technology |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132020000100316 |
Resumo: | Abstract Obesity is the most common chronic disease, due to its ignorance in society. It gives birth to other diseases such as endocrine. The objective of this research is to analyze the different trends of each BMI category and predict its related serious consequences. Data mining based Support Vector Machine (SVM) technique has been applied for this and the accuracy of each BMI category has been calculated using Receiver Operating Characteristics (ROC), which is an effective method and potentially applied to medical data sets. The Area Under Curve (AUC) of ROC and predictive accuracy have been calculated for each classified BMI category. Our analysis shows interesting results and it is found that BMI ≥ 25 has the highest AUC and Predictive accuracy compares to other BMI, which claims a good rank of performance. From our trends, it has been explored that at each BMI precaution is mandatory even if the BMI < 18.5 and at ideal BMI too. Development of effective awareness, early monitoring and interventions can prevent its harmful effects on health. |
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Brazilian Archives of Biology and Technology |
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Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesitydata miningsupport vector machinereceiver operating characteristicsarea under curvebody mass indexobesityAbstract Obesity is the most common chronic disease, due to its ignorance in society. It gives birth to other diseases such as endocrine. The objective of this research is to analyze the different trends of each BMI category and predict its related serious consequences. Data mining based Support Vector Machine (SVM) technique has been applied for this and the accuracy of each BMI category has been calculated using Receiver Operating Characteristics (ROC), which is an effective method and potentially applied to medical data sets. The Area Under Curve (AUC) of ROC and predictive accuracy have been calculated for each classified BMI category. Our analysis shows interesting results and it is found that BMI ≥ 25 has the highest AUC and Predictive accuracy compares to other BMI, which claims a good rank of performance. From our trends, it has been explored that at each BMI precaution is mandatory even if the BMI < 18.5 and at ideal BMI too. Development of effective awareness, early monitoring and interventions can prevent its harmful effects on health.Instituto de Tecnologia do Paraná - Tecpar2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132020000100316Brazilian Archives of Biology and Technology v.63 2020reponame:Brazilian Archives of Biology and Technologyinstname:Instituto de Tecnologia do Paraná (Tecpar)instacron:TECPAR10.1590/1678-4324-2020190736info:eu-repo/semantics/openAccessSiddiqui,Mohammad KhubebMorales-Menendez,RubenAhmad,Sultaneng2020-08-27T00:00:00Zoai:scielo:S1516-89132020000100316Revistahttps://www.scielo.br/j/babt/https://old.scielo.br/oai/scielo-oai.phpbabt@tecpar.br||babt@tecpar.br1678-43241516-8913opendoar:2020-08-27T00:00Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar)false |
dc.title.none.fl_str_mv |
Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity |
title |
Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity |
spellingShingle |
Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity Siddiqui,Mohammad Khubeb data mining support vector machine receiver operating characteristics area under curve body mass index obesity |
title_short |
Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity |
title_full |
Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity |
title_fullStr |
Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity |
title_full_unstemmed |
Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity |
title_sort |
Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity |
author |
Siddiqui,Mohammad Khubeb |
author_facet |
Siddiqui,Mohammad Khubeb Morales-Menendez,Ruben Ahmad,Sultan |
author_role |
author |
author2 |
Morales-Menendez,Ruben Ahmad,Sultan |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Siddiqui,Mohammad Khubeb Morales-Menendez,Ruben Ahmad,Sultan |
dc.subject.por.fl_str_mv |
data mining support vector machine receiver operating characteristics area under curve body mass index obesity |
topic |
data mining support vector machine receiver operating characteristics area under curve body mass index obesity |
description |
Abstract Obesity is the most common chronic disease, due to its ignorance in society. It gives birth to other diseases such as endocrine. The objective of this research is to analyze the different trends of each BMI category and predict its related serious consequences. Data mining based Support Vector Machine (SVM) technique has been applied for this and the accuracy of each BMI category has been calculated using Receiver Operating Characteristics (ROC), which is an effective method and potentially applied to medical data sets. The Area Under Curve (AUC) of ROC and predictive accuracy have been calculated for each classified BMI category. Our analysis shows interesting results and it is found that BMI ≥ 25 has the highest AUC and Predictive accuracy compares to other BMI, which claims a good rank of performance. From our trends, it has been explored that at each BMI precaution is mandatory even if the BMI < 18.5 and at ideal BMI too. Development of effective awareness, early monitoring and interventions can prevent its harmful effects on health. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132020000100316 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132020000100316 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-4324-2020190736 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Instituto de Tecnologia do Paraná - Tecpar |
publisher.none.fl_str_mv |
Instituto de Tecnologia do Paraná - Tecpar |
dc.source.none.fl_str_mv |
Brazilian Archives of Biology and Technology v.63 2020 reponame:Brazilian Archives of Biology and Technology instname:Instituto de Tecnologia do Paraná (Tecpar) instacron:TECPAR |
instname_str |
Instituto de Tecnologia do Paraná (Tecpar) |
instacron_str |
TECPAR |
institution |
TECPAR |
reponame_str |
Brazilian Archives of Biology and Technology |
collection |
Brazilian Archives of Biology and Technology |
repository.name.fl_str_mv |
Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar) |
repository.mail.fl_str_mv |
babt@tecpar.br||babt@tecpar.br |
_version_ |
1750318279686094848 |