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May 1, 2026AJPM FocusOpen Access

Beyond Accuracy: Machine Learning Models for Predicting Presence of Permanent Molar Caries in U.S. Children and Adolescents with Fairness Consideration

Pritam Deb Lin Li Christina Scherrer

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Key Points

The research aims to evaluate machine learning models for predicting the presence of molar caries while ensuring fairness across different demographic groups.
Utilized tree-based machine learning models for prediction.
Balanced fairness and accuracy metrics to assess model performance.
Data focused on U.S. children and adolescents.
Tree-based models achieved the best balance between fairness and accuracy.
Significant improvements in prediction rates among diverse demographic groups were observed.

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Implication

Demonstrates improved prediction of molar caries in children, indicating enhanced fairness in outcomes.

Abstract

Tree-based machine learning models balanced fairness and accuracy best.

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Cite This Study

Deb et al. (2026) studied this question.

synapsesocial.com/papers/6a02c2b9ce8c8c81e9640371 https://doi.org/10.1016/j.focus.2026.100506

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