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Interpretable Machine Learning for Population-Level Severe Tooth Loss Prediction: A Two-Axis External Validation

2026-04-05

Abstract excerpt

<h4>Objectives</h4> Machine learning can predict severe tooth loss (STL, ≥6 missing teeth), but opaque black-box models neglecting complex survey designs limit clinical adoption. This study developed and externally validated an intrinsically interpretable, survey-weighted framework for population-level STL prediction, capturing complex socio-behavioral and systemic health determinants. <h4>Methods</h4> We analyz...

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Literature Corpus work
9db0d072-767b-595c-aa25-faa7cdf8e4d9
DOI
10.64898/2026.04.03.26350106
Open publication

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Interpretable Machine Learning for Population-Level Severe Tooth Loss Prediction: A Two-Axis External ValidationDOI 10.64898/2026.04.03.26350106
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