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Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7

Background: Dental caries is the most common chronic childhood infectious disease and is a serious public health problem affecting both developing and industrialized countries, yet it is preventable in most cases. This study evaluated the potential of screening for dental caries among children using...

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Autores principales: Ramos-Gomez, Francisco, Marcus, Marvin, Maida, Carl A., Wang, Yan, Kinsler, Janni J., Xiong, Di, Lee, Steve Y., Hays, Ron D., Shen, Jie, Crall, James J., Liu, Honghu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700143/
https://www.ncbi.nlm.nih.gov/pubmed/34940038
http://dx.doi.org/10.3390/dj9120141
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author Ramos-Gomez, Francisco
Marcus, Marvin
Maida, Carl A.
Wang, Yan
Kinsler, Janni J.
Xiong, Di
Lee, Steve Y.
Hays, Ron D.
Shen, Jie
Crall, James J.
Liu, Honghu
author_facet Ramos-Gomez, Francisco
Marcus, Marvin
Maida, Carl A.
Wang, Yan
Kinsler, Janni J.
Xiong, Di
Lee, Steve Y.
Hays, Ron D.
Shen, Jie
Crall, James J.
Liu, Honghu
author_sort Ramos-Gomez, Francisco
collection PubMed
description Background: Dental caries is the most common chronic childhood infectious disease and is a serious public health problem affecting both developing and industrialized countries, yet it is preventable in most cases. This study evaluated the potential of screening for dental caries among children using a machine learning algorithm applied to parent perceptions of their child’s oral health assessed by survey. Methods: The sample consisted of 182 parents/caregivers and their children 2–7 years of age living in Los Angeles County. Random forest (a machine learning algorithm) was used to identify survey items that were predictors of active caries and caries experience. We applied a three-fold cross-validation method. A threshold was determined by maximizing the sum of sensitivity and specificity conditional on the sensitivity of at least 70%. The importance of survey items to classifying active caries and caries experience was measured using mean decreased Gini (MDG) and mean decreased accuracy (MDA) coefficients. Results: Survey items that were strong predictors of active caries included parent’s age (MDG = 0.84; MDA = 1.97), unmet needs (MDG = 0.71; MDA = 2.06) and the child being African American (MDG = 0.38; MDA = 1.92). Survey items that were strong predictors of caries experience included parent’s age (MDG = 2.97; MDA = 4.74), child had an oral health problem in the past 12 months (MDG = 2.20; MDA = 4.04) and child had a tooth that hurt (MDG = 1.65; MDA = 3.84). Conclusion: Our findings demonstrate the potential of screening for active caries and caries experience among children using surveys answered by their parents.
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spelling pubmed-87001432021-12-24 Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7 Ramos-Gomez, Francisco Marcus, Marvin Maida, Carl A. Wang, Yan Kinsler, Janni J. Xiong, Di Lee, Steve Y. Hays, Ron D. Shen, Jie Crall, James J. Liu, Honghu Dent J (Basel) Article Background: Dental caries is the most common chronic childhood infectious disease and is a serious public health problem affecting both developing and industrialized countries, yet it is preventable in most cases. This study evaluated the potential of screening for dental caries among children using a machine learning algorithm applied to parent perceptions of their child’s oral health assessed by survey. Methods: The sample consisted of 182 parents/caregivers and their children 2–7 years of age living in Los Angeles County. Random forest (a machine learning algorithm) was used to identify survey items that were predictors of active caries and caries experience. We applied a three-fold cross-validation method. A threshold was determined by maximizing the sum of sensitivity and specificity conditional on the sensitivity of at least 70%. The importance of survey items to classifying active caries and caries experience was measured using mean decreased Gini (MDG) and mean decreased accuracy (MDA) coefficients. Results: Survey items that were strong predictors of active caries included parent’s age (MDG = 0.84; MDA = 1.97), unmet needs (MDG = 0.71; MDA = 2.06) and the child being African American (MDG = 0.38; MDA = 1.92). Survey items that were strong predictors of caries experience included parent’s age (MDG = 2.97; MDA = 4.74), child had an oral health problem in the past 12 months (MDG = 2.20; MDA = 4.04) and child had a tooth that hurt (MDG = 1.65; MDA = 3.84). Conclusion: Our findings demonstrate the potential of screening for active caries and caries experience among children using surveys answered by their parents. MDPI 2021-12-01 /pmc/articles/PMC8700143/ /pubmed/34940038 http://dx.doi.org/10.3390/dj9120141 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ramos-Gomez, Francisco
Marcus, Marvin
Maida, Carl A.
Wang, Yan
Kinsler, Janni J.
Xiong, Di
Lee, Steve Y.
Hays, Ron D.
Shen, Jie
Crall, James J.
Liu, Honghu
Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7
title Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7
title_full Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7
title_fullStr Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7
title_full_unstemmed Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7
title_short Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7
title_sort using a machine learning algorithm to predict the likelihood of presence of dental caries among children aged 2 to 7
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700143/
https://www.ncbi.nlm.nih.gov/pubmed/34940038
http://dx.doi.org/10.3390/dj9120141
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