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February 9, 2026International Dental Journal1 citationsOpen Access

Developing an Interpretable Machine Learning Framework to Predict and Analyse Early Childhood Caries in Children Aged 2 to 6 Years: A Single-centre Observational Study

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XYXinyue YuanYCYiting ChuWCWenyan Cai

Key Points

  • This research aims to develop a machine learning framework to identify key factors influencing early childhood caries.
  • Developed machine learning models to analyze data
  • Focused on children aged 2 to 6 years
  • Examined determinants such as maternal education and dietary patterns
  • Utilized risk-based stratification for analysis
  • Identified maternal education as a significant risk factor
  • Dietary patterns were linked to higher ECC risk
  • Lifestyle-related factors also played a critical role
  • Risk stratification can guide preventive efforts

Abstract

The machine learning models effectively identified key determinants of ECC, underscoring the critical roles of maternal education, dietary patterns, and lifestyle-related factors. Risk-based stratification derived from these models may inform targeted early preventive interventions in both clinical and community settings, thereby contributing to a reduction in the overall burden of ECC.

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Cite This Study

Yuan et al. (2026) studied this question.

synapsesocial.com/papers/698978dff0ec2af6756e7298https://doi.org/10.1016/j.identj.2026.109419
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