NDLI: Predicting dental caries outcomes in young adults using machine learning approach

Content Provider	Springer Nature : BioMed Central
Author	Ogwo, Chukwuebuka Brown, Grant Warren, John Caplan, Daniel Levy, Steven
Abstract	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 D2+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 D2+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 (R2) 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.
Related Links	https://bmcoralhealth.biomedcentral.com/counter/pdf/10.1186/s12903-024-04294-7.pdf
Ending Page	9
Page Count	9
Starting Page	1
File Format	HTM / HTML
ISSN	14726831
DOI	10.1186/s12903-024-04294-7
Journal	BMC Oral Health
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-05-03
Access Restriction	Open
Subject Keyword	Dentistry Oral and Maxillofacial Surgery Machine learning Dental Caries Longitudinal Artificial intelliegence Prediction
Content Type	Text
Resource Type	Article
Subject	Dentistry
Journal Impact Factor	2.6/2023
5-Year Journal Impact Factor	3.2/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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