Explainable Machine Learning for Pediatric Dental Risk Stratification Using Socio-Demographic Determinants
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| author | Kanade, Manasi Thakkar, Abhi Fernandes, Gabriela |
| author_facet | Kanade, Manasi Thakkar, Abhi Fernandes, Gabriela |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12405 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Explainable Machine Learning for Pediatric Dental Risk Stratification Using Socio-Demographic Determinants Kanade, Manasi Thakkar, Abhi Fernandes, Gabriela Machine Learning Artificial Intelligence 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. |
| title | Explainable Machine Learning for Pediatric Dental Risk Stratification Using Socio-Demographic Determinants |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2601.12405 |