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

ABSTRACT:

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 select

SUBMITTER: Wu L 

PROVIDER: S-EPMC12706944 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature