A Fuzzy Approach for Diabetes Mellitus Type 2 Classification
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=S1516-89132020000100404 |
Resumo: | Abstract This paper proposes an automatic fuzzy classification system for glycemic index, which indicates the level of Diabetes Mellitus type 2. Diabetes is a chronic disease occurred when there is deficiency in insulin production or in its action, or both, causing complications. Neuro-fuzzy systems and Decision Trees are used to obtain, respectively, the numerical parameters of the membership functions and the linguistic based rules of the fuzzy classification system. The results goal to categorize the glycemic index into 4 classes: decrease a lot, decrease, stable and increase. Real database from [1] is used and the input attributes of the system are defined. In addition, the proposed automatic fuzzy classification system is compared with an “expert” fuzzy classification system, which is totally modeled using expert knowledge. From linguistic based rules obtained from fuzzy inference process, new scenarios are simulated in order to obtain a larger data set which provides a better evaluation of the classification systems. Results are promising, since they indicate the best treatment - intervention or comparative - for each patient, assisting in the decision-making process of the health care professional. |
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A Fuzzy Approach for Diabetes Mellitus Type 2 ClassificationGlycemic IndexFuzzy ClassificationDecision TreeNeuro-fuzzy Abstract This paper proposes an automatic fuzzy classification system for glycemic index, which indicates the level of Diabetes Mellitus type 2. Diabetes is a chronic disease occurred when there is deficiency in insulin production or in its action, or both, causing complications. Neuro-fuzzy systems and Decision Trees are used to obtain, respectively, the numerical parameters of the membership functions and the linguistic based rules of the fuzzy classification system. The results goal to categorize the glycemic index into 4 classes: decrease a lot, decrease, stable and increase. Real database from [1] is used and the input attributes of the system are defined. In addition, the proposed automatic fuzzy classification system is compared with an “expert” fuzzy classification system, which is totally modeled using expert knowledge. From linguistic based rules obtained from fuzzy inference process, new scenarios are simulated in order to obtain a larger data set which provides a better evaluation of the classification systems. Results are promising, since they indicate the best treatment - intervention or comparative - for each patient, assisting in the decision-making process of the health care professional.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=S1516-89132020000100404Brazilian Archives of Biology and Technology v.63 2020reponame:Brazilian Archives of Biology and Technologyinstname:Instituto de Tecnologia do Paraná (Tecpar)instacron:TECPAR10.1590/1678-4324-2020180742info:eu-repo/semantics/openAccessBressan,Glaucia MariaAzevedo,Beatriz Cristina Flamia deSouza,Roberto Molina deeng2020-05-04T00:00:00Zoai:scielo:S1516-89132020000100404Revistahttps://www.scielo.br/j/babt/https://old.scielo.br/oai/scielo-oai.phpbabt@tecpar.br||babt@tecpar.br1678-43241516-8913opendoar:2020-05-04T00:00Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar)false |
dc.title.none.fl_str_mv |
A Fuzzy Approach for Diabetes Mellitus Type 2 Classification |
title |
A Fuzzy Approach for Diabetes Mellitus Type 2 Classification |
spellingShingle |
A Fuzzy Approach for Diabetes Mellitus Type 2 Classification Bressan,Glaucia Maria Glycemic Index Fuzzy Classification Decision Tree Neuro-fuzzy |
title_short |
A Fuzzy Approach for Diabetes Mellitus Type 2 Classification |
title_full |
A Fuzzy Approach for Diabetes Mellitus Type 2 Classification |
title_fullStr |
A Fuzzy Approach for Diabetes Mellitus Type 2 Classification |
title_full_unstemmed |
A Fuzzy Approach for Diabetes Mellitus Type 2 Classification |
title_sort |
A Fuzzy Approach for Diabetes Mellitus Type 2 Classification |
author |
Bressan,Glaucia Maria |
author_facet |
Bressan,Glaucia Maria Azevedo,Beatriz Cristina Flamia de Souza,Roberto Molina de |
author_role |
author |
author2 |
Azevedo,Beatriz Cristina Flamia de Souza,Roberto Molina de |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Bressan,Glaucia Maria Azevedo,Beatriz Cristina Flamia de Souza,Roberto Molina de |
dc.subject.por.fl_str_mv |
Glycemic Index Fuzzy Classification Decision Tree Neuro-fuzzy |
topic |
Glycemic Index Fuzzy Classification Decision Tree Neuro-fuzzy |
description |
Abstract This paper proposes an automatic fuzzy classification system for glycemic index, which indicates the level of Diabetes Mellitus type 2. Diabetes is a chronic disease occurred when there is deficiency in insulin production or in its action, or both, causing complications. Neuro-fuzzy systems and Decision Trees are used to obtain, respectively, the numerical parameters of the membership functions and the linguistic based rules of the fuzzy classification system. The results goal to categorize the glycemic index into 4 classes: decrease a lot, decrease, stable and increase. Real database from [1] is used and the input attributes of the system are defined. In addition, the proposed automatic fuzzy classification system is compared with an “expert” fuzzy classification system, which is totally modeled using expert knowledge. From linguistic based rules obtained from fuzzy inference process, new scenarios are simulated in order to obtain a larger data set which provides a better evaluation of the classification systems. Results are promising, since they indicate the best treatment - intervention or comparative - for each patient, assisting in the decision-making process of the health care professional. |
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=S1516-89132020000100404 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132020000100404 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-4324-2020180742 |
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_ |
1750318280003813376 |