Model for Predicting Temporomandibular Dysfunction: Use of Classification Tree Analysis
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Dental Journal |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-64402020000400360 |
Resumo: | Abstract The aim of this study was to construct a predictive model that uses classification tree statistical analysis to predict the occurrence of temporomandibular disorder, by dividing the sample into groups of high and low risk for the development of the disease. The use of predictive statistical approaches that facilitate the process of recognizing and/or predicting the occurrence of temporomandibular disorder is of interest to the scientific community, for the purpose of providing patients with more adequate solutions in each case. This was a cross-sectional analytical population-based study that involved a sample of 776 individuals who had sought medical or dental attendance at the Family Health Units in Recife, PE, Brazil. The sample was submitted to anamnesis using the instrument Research Diagnostic Criteria for Temporomandibular Disorders. The data were inserted into the software Statistical Package for the Social Sciences 20.0 and analyzed by the Pearson Chi-square test for bivariate analysis, and by the classification tree method for the multivariate analysis. Temporomandibular disorder could be predicted by orofacial pain, age and depression. The high-risk group was composed of individuals with orofacial pain, those between the ages of 25 and 59 years and those who presented depression. The low risk group was composed of individuals without orofacial pain. The authors were able to conclude that the best predictor for temporomandibular disorder was orofacial pain, and that the predictive model proposed by the classification tree could be applied as a tool for simplifying decision making relative to the occurrence of temporomandibular disorder. |
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Brazilian Dental Journal |
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Model for Predicting Temporomandibular Dysfunction: Use of Classification Tree Analysistemporomandibular joint dysfunction syndromeanalysis of decisionsdecision treesdecision support techniquesAbstract The aim of this study was to construct a predictive model that uses classification tree statistical analysis to predict the occurrence of temporomandibular disorder, by dividing the sample into groups of high and low risk for the development of the disease. The use of predictive statistical approaches that facilitate the process of recognizing and/or predicting the occurrence of temporomandibular disorder is of interest to the scientific community, for the purpose of providing patients with more adequate solutions in each case. This was a cross-sectional analytical population-based study that involved a sample of 776 individuals who had sought medical or dental attendance at the Family Health Units in Recife, PE, Brazil. The sample was submitted to anamnesis using the instrument Research Diagnostic Criteria for Temporomandibular Disorders. The data were inserted into the software Statistical Package for the Social Sciences 20.0 and analyzed by the Pearson Chi-square test for bivariate analysis, and by the classification tree method for the multivariate analysis. Temporomandibular disorder could be predicted by orofacial pain, age and depression. The high-risk group was composed of individuals with orofacial pain, those between the ages of 25 and 59 years and those who presented depression. The low risk group was composed of individuals without orofacial pain. The authors were able to conclude that the best predictor for temporomandibular disorder was orofacial pain, and that the predictive model proposed by the classification tree could be applied as a tool for simplifying decision making relative to the occurrence of temporomandibular disorder.Fundação Odontológica de Ribeirão Preto2020-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-64402020000400360Brazilian Dental Journal v.31 n.4 2020reponame:Brazilian Dental Journalinstname:Fundação Odontológica de Ribeirão Preto (FUNORP)instacron:FUNORP10.1590/0103-6440202003279info:eu-repo/semantics/openAccessWaked,Jorge PCanuto,Mariana P. L. de A. M.Gueiros,Maria Cecilia S. N.Aroucha,João Marcílio C. N. L.Farias,Cleysiane G.Caldas Jr,Arnaldo de F.eng2020-09-01T00:00:00Zoai:scielo:S0103-64402020000400360Revistahttps://www.scielo.br/j/bdj/https://old.scielo.br/oai/scielo-oai.phpbdj@forp.usp.br||sergio@fosjc.unesp.br1806-47600103-6440opendoar:2020-09-01T00:00Brazilian Dental Journal - Fundação Odontológica de Ribeirão Preto (FUNORP)false |
dc.title.none.fl_str_mv |
Model for Predicting Temporomandibular Dysfunction: Use of Classification Tree Analysis |
title |
Model for Predicting Temporomandibular Dysfunction: Use of Classification Tree Analysis |
spellingShingle |
Model for Predicting Temporomandibular Dysfunction: Use of Classification Tree Analysis Waked,Jorge P temporomandibular joint dysfunction syndrome analysis of decisions decision trees decision support techniques |
title_short |
Model for Predicting Temporomandibular Dysfunction: Use of Classification Tree Analysis |
title_full |
Model for Predicting Temporomandibular Dysfunction: Use of Classification Tree Analysis |
title_fullStr |
Model for Predicting Temporomandibular Dysfunction: Use of Classification Tree Analysis |
title_full_unstemmed |
Model for Predicting Temporomandibular Dysfunction: Use of Classification Tree Analysis |
title_sort |
Model for Predicting Temporomandibular Dysfunction: Use of Classification Tree Analysis |
author |
Waked,Jorge P |
author_facet |
Waked,Jorge P Canuto,Mariana P. L. de A. M. Gueiros,Maria Cecilia S. N. Aroucha,João Marcílio C. N. L. Farias,Cleysiane G. Caldas Jr,Arnaldo de F. |
author_role |
author |
author2 |
Canuto,Mariana P. L. de A. M. Gueiros,Maria Cecilia S. N. Aroucha,João Marcílio C. N. L. Farias,Cleysiane G. Caldas Jr,Arnaldo de F. |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Waked,Jorge P Canuto,Mariana P. L. de A. M. Gueiros,Maria Cecilia S. N. Aroucha,João Marcílio C. N. L. Farias,Cleysiane G. Caldas Jr,Arnaldo de F. |
dc.subject.por.fl_str_mv |
temporomandibular joint dysfunction syndrome analysis of decisions decision trees decision support techniques |
topic |
temporomandibular joint dysfunction syndrome analysis of decisions decision trees decision support techniques |
description |
Abstract The aim of this study was to construct a predictive model that uses classification tree statistical analysis to predict the occurrence of temporomandibular disorder, by dividing the sample into groups of high and low risk for the development of the disease. The use of predictive statistical approaches that facilitate the process of recognizing and/or predicting the occurrence of temporomandibular disorder is of interest to the scientific community, for the purpose of providing patients with more adequate solutions in each case. This was a cross-sectional analytical population-based study that involved a sample of 776 individuals who had sought medical or dental attendance at the Family Health Units in Recife, PE, Brazil. The sample was submitted to anamnesis using the instrument Research Diagnostic Criteria for Temporomandibular Disorders. The data were inserted into the software Statistical Package for the Social Sciences 20.0 and analyzed by the Pearson Chi-square test for bivariate analysis, and by the classification tree method for the multivariate analysis. Temporomandibular disorder could be predicted by orofacial pain, age and depression. The high-risk group was composed of individuals with orofacial pain, those between the ages of 25 and 59 years and those who presented depression. The low risk group was composed of individuals without orofacial pain. The authors were able to conclude that the best predictor for temporomandibular disorder was orofacial pain, and that the predictive model proposed by the classification tree could be applied as a tool for simplifying decision making relative to the occurrence of temporomandibular disorder. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-08-01 |
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=S0103-64402020000400360 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-64402020000400360 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0103-6440202003279 |
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 |
Fundação Odontológica de Ribeirão Preto |
publisher.none.fl_str_mv |
Fundação Odontológica de Ribeirão Preto |
dc.source.none.fl_str_mv |
Brazilian Dental Journal v.31 n.4 2020 reponame:Brazilian Dental Journal instname:Fundação Odontológica de Ribeirão Preto (FUNORP) instacron:FUNORP |
instname_str |
Fundação Odontológica de Ribeirão Preto (FUNORP) |
instacron_str |
FUNORP |
institution |
FUNORP |
reponame_str |
Brazilian Dental Journal |
collection |
Brazilian Dental Journal |
repository.name.fl_str_mv |
Brazilian Dental Journal - Fundação Odontológica de Ribeirão Preto (FUNORP) |
repository.mail.fl_str_mv |
bdj@forp.usp.br||sergio@fosjc.unesp.br |
_version_ |
1754204096100827136 |