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

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