Machine Learning-Based Prediction of Dental Caries Risk in Preschool Children Using Data from the CAMBRA-Kids Mobile Application

Early childhood caries risk is dynamic and can change over relatively short periods, even in the presence of preventive interventions. This study aimed to predict caries risk transitions in preschoolers using longitudinal data from the CAMBRA-kids mobile application. Using machine learning, we identified children whose risk progressed to high or extreme categories over 12 months and clarified the key contributing factors. A Random Forest model was developed using a multidimensional dataset that integrated parent-reported behavioral data and clinical assessments. Model performance was evaluated through ROC and precision–recall (PR) analyses, while SHAP was employed to ensure model interpretability and identify influential variables. Despite improvements in disease indicators and risk factors overall following the intervention, a subset of children transitioned to high or extreme risk. The model demonstrated acceptable discriminative performance with high precision in an imbalanced dataset. Changes in quantitative light-induced fluorescence loss, restored teeth, and red-fluorescent plaque area were identified as key predictors. These findings suggest that caries risk escalation reflects cumulative biological and clinical changes rather than short-term behavioral fluctuations and support the use of longitudinal, explainable machine learning for early risk identification and targeted prevention.