Interpretable Machine Learning for Population-Level Severe Tooth Loss Prediction: A Two-Axis External Validation
This study presents a survey-weighted, intrinsically interpretable machine learning framework using Explainable Boosting Machines that robustly predicts population-level severe tooth loss across temporal and clinical cohorts while maintaining complete transparency and superior clinical utility compared to black-box alternatives.