Explainable Machine Learning for Pediatric Dental Risk Stratification Using Socio-Demographic Determinants

TL;DR

This study develops an explainable machine learning model for pediatric dental risk assessment using socio-demographic data, emphasizing interpretability and ethical considerations over maximal accuracy.

Contribution

It introduces an interpretable ML framework that incorporates socio-demographic factors for pediatric dental risk stratification, prioritizing transparency and ethical deployment.

Findings

Model achieved AUC of 0.61 indicating modest discrimination.

SHAP analysis identified age and income-to-poverty ratio as key risk factors.

Model demonstrated conservative calibration, underestimating high-risk probabilities.

Abstract

Background: Pediatric dental disease remains one of the most prevalent and inequitable chronic health conditions worldwide. Although strong epidemiological evidence links oral health outcomes to socio-economic and demographic determinants, most artificial intelligence (AI) applications in dentistry rely on image-based diagnosis and black-box prediction models, limiting transparency and ethical applicability in pediatric populations. Objective: This study aimed to develop and evaluate an explainable machine learning framework for pediatric dental risk stratification that prioritizes interpretability, calibration, and ethical deployment over maximal predictive accuracy. Methods: A supervised machine learning model was trained using population-level pediatric data including age, income-to-poverty ratio, race/ethnicity, gender, and medical history. Model performance was assessed using receiver operating characteristic (ROC) analysis and calibration curves. Explainability was achieved using SHapley Additive exPlanations (SHAP) to provide global and individual-level interpretation of predictions. Results: The model achieved modest discrimination (AUC = 0.61) with conservative calibration, underestimating risk at higher probability levels. SHAP analysis identified age and income-to-poverty ratio as the strongest contributors to predicted risk, followed by race/ethnicity and gender. Conclusion: Explainable machine learning enables transparent, prevention-oriented pediatric dental risk stratification and supports population screening and equitable resource allocation rather than diagnostic decision-making.