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.

Keywords: Decayed-missing-field-teeth, Decision tree algorithm, Machine learning, Prediction, Random forest algorithm