J Korean Acad Oral Health.  2020 Mar;44(1):55-63. 10.11149/jkaoh.2020.44.1.55.

Prediction of dental caries in 12-year-old children using machine-learning algorithms

• Yang YH
• Kim JS
• Jeong SH

• KMID: 2471985
• DOI: http://doi.org/10.11149/jkaoh.2020.44.1.55

Abstract

OBJECTIVES
The decayed-missing-filled (DMFT) index is a representative oral health indicator. Prediction of DMFT index is an important basis for the development of public oral health care projects and strategies for caries prevention. In this study, we used data from the 2015 Korean children's oral health survey to predict DMFT index and caries risk groups using statistical techniques and four different machine-learning algorithms.
METHODS
DMFT prediction models were constructed using multiple linear regression and four different machine-learning algorithms: decision tree regressor, decision tree classifier (DTC), random forest regressor, and random forest classifier (RFC). Thereafter, their accuracies were compared.
RESULTS
For the DMFT predictive model, the prediction accuracy of multiple linear regression and RFC were 15.24% and 43.27%, respectively. The accuracy of DTC prediction was 2.84 times that of multiple linear regression. The important feature of the machine-learning model, which predicts DMFT index and the caries risk group, was the number of teeth with sealants.
CONCLUSIONS
Using data from the 2015 Korean children's oral health survey, which is considered big data in the field of oral health survey in Korea, this study confirmed that machine-learning models are more useful than statistical models for predicting DMFT index and caries risk in 12-year-old children. Therefore, it is expected that the machine-learning model can be used to predict the DMFT score.

Keyword

Random forest algorithm; Prediction; Machine learning; Decision tree algorithm; Decayed-missing-field-teeth

MeSH Terms

Child*
Decision Trees
Dental Caries*
Forests
Humans
Korea
Linear Models
Machine Learning
Models, Statistical
Oral Health
Tooth

Reference

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