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Predicting Dental Caries Outcomes in Young Adults Using Machine Learning Approach
OBJECTIVES: To predict the dental caries outcomes in young adults from a set of longitudinally-obtained predictor variables and identify the most important predictors using machine learning techniques. METHODS: This study was conducted using the Iowa Fluoride Study dataset. The predictor variables -...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Journal Experts
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602064/ https://www.ncbi.nlm.nih.gov/pubmed/37886508 http://dx.doi.org/10.21203/rs.3.rs-3393538/v1 |
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author | Ogwo, Chukwuebuka Grant, Brown Warren, John Caplan, Daniel Levy, Steven |
author_facet | Ogwo, Chukwuebuka Grant, Brown Warren, John Caplan, Daniel Levy, Steven |
author_sort | Ogwo, Chukwuebuka |
collection | PubMed |
description | OBJECTIVES: To predict the dental caries outcomes in young adults from a set of longitudinally-obtained predictor variables and identify the most important predictors using machine learning techniques. METHODS: This study was conducted using the Iowa Fluoride Study dataset. The predictor variables - sex, mother’s education, family income, composite socio-economic status (SES), caries experience at ages 9, 13, and 17, and the cumulative estimates of risk and protective factors, including fluoride, dietary, and behavioral variables from ages 5–9, 9–13, 13–17, and 17–23 were used to predict the age 23 D(2+)MFS count. The following machine learning models (LASSO regression, generalized boosting machines (GBM), negative binomial (NegGLM), and extreme gradient boosting models (XGBOOST)) were compared under 5-fold cross validation with nested resampling techniques. RESULTS: The prevalence of cavitated level caries experience at age 23 (mean D(2+)MFS count) was 4.75. The predictive analysis found LASSO to be the best performing model (compared to GBM, NegGLM, and XGBOOST), with a root mean square error (RMSE) of 0.70, and coefficient of determination (R(2)) of 0.44. After dichotomization of the predicted and observed values of the LASSO regression, the classification results showed accuracy, precision, recall, and ROC AUC of 83.7%, 85.9%, 93.1%, 68.2%, respectively. Previous caries experience at age 13 and age 17 and sugar-sweetened beverages intakes at age 13 and age 17 were found to be the four most important predictors of cavitated caries count at age 23. CONCLUSION: Our machine learning model showed high accuracy and precision in the prediction of caries in young adults from a longitudinally-obtained predictor variables. Our model could, in the future, after further development and validation with other diverse population data, be used by public health specialists and policy-makers as a screening tool to identify the risk of caries in young adults and apply more targeted interventions. However, data from a more diverse population are needed to improve the quality and generalizability of caries prediction. |
format | Online Article Text |
id | pubmed-10602064 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Journal Experts |
record_format | MEDLINE/PubMed |
spelling | pubmed-106020642023-10-27 Predicting Dental Caries Outcomes in Young Adults Using Machine Learning Approach Ogwo, Chukwuebuka Grant, Brown Warren, John Caplan, Daniel Levy, Steven Res Sq Article OBJECTIVES: To predict the dental caries outcomes in young adults from a set of longitudinally-obtained predictor variables and identify the most important predictors using machine learning techniques. METHODS: This study was conducted using the Iowa Fluoride Study dataset. The predictor variables - sex, mother’s education, family income, composite socio-economic status (SES), caries experience at ages 9, 13, and 17, and the cumulative estimates of risk and protective factors, including fluoride, dietary, and behavioral variables from ages 5–9, 9–13, 13–17, and 17–23 were used to predict the age 23 D(2+)MFS count. The following machine learning models (LASSO regression, generalized boosting machines (GBM), negative binomial (NegGLM), and extreme gradient boosting models (XGBOOST)) were compared under 5-fold cross validation with nested resampling techniques. RESULTS: The prevalence of cavitated level caries experience at age 23 (mean D(2+)MFS count) was 4.75. The predictive analysis found LASSO to be the best performing model (compared to GBM, NegGLM, and XGBOOST), with a root mean square error (RMSE) of 0.70, and coefficient of determination (R(2)) of 0.44. After dichotomization of the predicted and observed values of the LASSO regression, the classification results showed accuracy, precision, recall, and ROC AUC of 83.7%, 85.9%, 93.1%, 68.2%, respectively. Previous caries experience at age 13 and age 17 and sugar-sweetened beverages intakes at age 13 and age 17 were found to be the four most important predictors of cavitated caries count at age 23. CONCLUSION: Our machine learning model showed high accuracy and precision in the prediction of caries in young adults from a longitudinally-obtained predictor variables. Our model could, in the future, after further development and validation with other diverse population data, be used by public health specialists and policy-makers as a screening tool to identify the risk of caries in young adults and apply more targeted interventions. However, data from a more diverse population are needed to improve the quality and generalizability of caries prediction. American Journal Experts 2023-10-06 /pmc/articles/PMC10602064/ /pubmed/37886508 http://dx.doi.org/10.21203/rs.3.rs-3393538/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Ogwo, Chukwuebuka Grant, Brown Warren, John Caplan, Daniel Levy, Steven Predicting Dental Caries Outcomes in Young Adults Using Machine Learning Approach |
title | Predicting Dental Caries Outcomes in Young Adults Using Machine Learning Approach |
title_full | Predicting Dental Caries Outcomes in Young Adults Using Machine Learning Approach |
title_fullStr | Predicting Dental Caries Outcomes in Young Adults Using Machine Learning Approach |
title_full_unstemmed | Predicting Dental Caries Outcomes in Young Adults Using Machine Learning Approach |
title_short | Predicting Dental Caries Outcomes in Young Adults Using Machine Learning Approach |
title_sort | predicting dental caries outcomes in young adults using machine learning approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602064/ https://www.ncbi.nlm.nih.gov/pubmed/37886508 http://dx.doi.org/10.21203/rs.3.rs-3393538/v1 |
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