Predictors of tooth loss: A machine learning approach.

<h4>Introduction</h4>Little is understood about the socioeconomic predictors of tooth loss, a condition that can negatively impact individual's quality of life. The goal of this study is to develop a machine-learning algorithm to predict complete and incremental tooth loss among adu...

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Autores principales:	Hawazin W Elani, André F M Batista, W Murray Thomson, Ichiro Kawachi, Alexandre D P Chiavegatto Filho
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d8c832c82e7847668cdf2a29f540c2f4
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spelling	oai:doaj.org-article:d8c832c82e7847668cdf2a29f540c2f42021-12-02T20:07:05ZPredictors of tooth loss: A machine learning approach.1932-620310.1371/journal.pone.0252873https://doaj.org/article/d8c832c82e7847668cdf2a29f540c2f42021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252873https://doaj.org/toc/1932-6203<h4>Introduction</h4>Little is understood about the socioeconomic predictors of tooth loss, a condition that can negatively impact individual's quality of life. The goal of this study is to develop a machine-learning algorithm to predict complete and incremental tooth loss among adults and to compare the predictive performance of these models.<h4>Methods</h4>We used data from the National Health and Nutrition Examination Survey from 2011 to 2014. We developed multiple machine-learning algorithms and assessed their predictive performances by examining the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values.<h4>Results</h4>The extreme gradient boosting trees presented the highest performance in the prediction of edentulism (AUC = 88.7%; 95%CI: 87.1, 90.2), the absence of a functional dentition (AUC = 88.3% 95%CI: 87.3,89.3) and for predicting missing any tooth (AUC = 83.2%; 95%CI, 82.0, 84.4). Although, as expected, age and routine dental care emerged as strong predictors of tooth loss, the machine learning approach identified additional predictors, including socioeconomic conditions. Indeed, the performance of models incorporating socioeconomic characteristics was better at predicting tooth loss than those relying on clinical dental indicators alone.<h4>Conclusions</h4>Future application of machine-learning algorithm, with longitudinal cohorts, for identification of individuals at risk for tooth loss could assist clinicians to prioritize interventions directed toward the prevention of tooth loss.Hawazin W ElaniAndré F M BatistaW Murray ThomsonIchiro KawachiAlexandre D P Chiavegatto FilhoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252873 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hawazin W Elani André F M Batista W Murray Thomson Ichiro Kawachi Alexandre D P Chiavegatto Filho Predictors of tooth loss: A machine learning approach.
description	<h4>Introduction</h4>Little is understood about the socioeconomic predictors of tooth loss, a condition that can negatively impact individual's quality of life. The goal of this study is to develop a machine-learning algorithm to predict complete and incremental tooth loss among adults and to compare the predictive performance of these models.<h4>Methods</h4>We used data from the National Health and Nutrition Examination Survey from 2011 to 2014. We developed multiple machine-learning algorithms and assessed their predictive performances by examining the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values.<h4>Results</h4>The extreme gradient boosting trees presented the highest performance in the prediction of edentulism (AUC = 88.7%; 95%CI: 87.1, 90.2), the absence of a functional dentition (AUC = 88.3% 95%CI: 87.3,89.3) and for predicting missing any tooth (AUC = 83.2%; 95%CI, 82.0, 84.4). Although, as expected, age and routine dental care emerged as strong predictors of tooth loss, the machine learning approach identified additional predictors, including socioeconomic conditions. Indeed, the performance of models incorporating socioeconomic characteristics was better at predicting tooth loss than those relying on clinical dental indicators alone.<h4>Conclusions</h4>Future application of machine-learning algorithm, with longitudinal cohorts, for identification of individuals at risk for tooth loss could assist clinicians to prioritize interventions directed toward the prevention of tooth loss.
format	article
author	Hawazin W Elani André F M Batista W Murray Thomson Ichiro Kawachi Alexandre D P Chiavegatto Filho
author_facet	Hawazin W Elani André F M Batista W Murray Thomson Ichiro Kawachi Alexandre D P Chiavegatto Filho
author_sort	Hawazin W Elani
title	Predictors of tooth loss: A machine learning approach.
title_short	Predictors of tooth loss: A machine learning approach.
title_full	Predictors of tooth loss: A machine learning approach.
title_fullStr	Predictors of tooth loss: A machine learning approach.
title_full_unstemmed	Predictors of tooth loss: A machine learning approach.
title_sort	predictors of tooth loss: a machine learning approach.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/d8c832c82e7847668cdf2a29f540c2f4
work_keys_str_mv	AT hawazinwelani predictorsoftoothlossamachinelearningapproach AT andrefmbatista predictorsoftoothlossamachinelearningapproach AT wmurraythomson predictorsoftoothlossamachinelearningapproach AT ichirokawachi predictorsoftoothlossamachinelearningapproach AT alexandredpchiavegattofilho predictorsoftoothlossamachinelearningapproach
_version_	1718375332087595008

Predictors of tooth loss: A machine learning approach.

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