Predicting plaque-gingivitis risk in schoolchildren using an interpretable machine learning model: a cross-sectional study.

BACKGROUND

This study aimed to develop an interpretable machine learning (ML) model for predicting plaque-induced gingivitis risk in schoolchildren using questionnaire data. To enhance the model's interpretation, SHapley Additive exPlanations (SHAP) method was applied to analyze and explain the risk factors associated with plaque-gingivitis.

MATERIALS AND

METHODS

Using multi-stage cluster random sampling, 1755 children aged 6-12 in Lanzhou were enrolled. Participants completed a 22-item questionnaire and underwent clinical dental examinations. The collected data were stratified and randomly divided into a training set (70%) and a testing set (30%), with an independent external validation cohort (n = 120) prospectively collected for generalizability assessment. Feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Six ML algorithms-Light Gradient Boosting Machine (LightGBM), random forest (RF), logistic regression (LR), eXtreme Gradient Boosting (XGBoost), decision tree (DT), and K-nearest neighbors (KNN)-were employed to process the data. The efficacy of each algorithm was evaluated using area under the curve (AUC), sensitivity (recall), specificity, accuracy, precision, F1-score and decision curve analysis. Using the SHAP method, all predictors of gingivitis prevalence in children were ranked by importance.

RESULTS

51.3% (901/1755) of the children were clinically diagnosed with plaque‑induced gingivitis. 11 key predictors were selected using LASSO regression to build the ML models. Among all models, the RF achieved the highest discrimination (training AUC: 0.991; testing AUC: 0.909), followed closely by LightGBM (training AUC: 0.970; testing AUC: 0.904). The RF model was selected as the optimal model and maintained generalizability (external validation AUC: 0.824). SHAP analysis identified key predictors ranked by importance, including brushing frequency, age, regular dental checkups, brushing time, gingival bleeding during brushing, and annual income.

CONCLUSION

An interpretable RF model accurately stratified gingivitis risk using self-reported factors. This ML-driven strategy may reduce reliance on resource-intensive clinical examinations, supporting scalable pediatric gingivitis prevention in resource-limited settings.