ArticleInternational journal of environmental research and public health2021
Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction models for dental caries in children and adolescents: a systematic review and meta-analysis.BMJ open · 2025Pooled it
- Artificial Intelligence in Pediatric Dentistry: Current Applications, Emerging Trends, and Future Directions.Dentistry journal · 2026Review
- Artificial Intelligence Applications in Pediatric Dentistry: A Comprehensive Review.Biomimetics (Basel, Switzerland) · 2026Review
- Supragingival Biomarker flora of Children With and Without Cariogenic Disease and Black Stains, Aged 3 to 6 Years.International dental journal · 2026Article
- Artificial Intelligence in Pediatric Dentistry: A Systematic Review and Meta-Analysis.Children (Basel, Switzerland) · 2026Review
- Metabolic profiling of severe early childhood caries and black stain: pathway and biomarker discovery in 3-6-year-olds.Frontiers in molecular biosciences · 2026Article
- Comparison of salivary statherin and beta-defensin-2 levels, oral health behaviors, and demographic factors in children with and without early childhood caries.BMC oral health · 2025Article
- Artificial Intelligence Tools in Dentistry: A Systematic Review on Their Application and Outcomes.Cureus · 2025Review
- Artificial intelligence (AI) in restorative dentistry: current trends and future prospects.BMC oral health · 2025Review
- Use machine learning to predict treatment outcome of early childhood caries.BMC oral health · 2025Article
- Global trends and challenges in childhood caries: a 20-year bibliometric review.Translational pediatrics · 2025Review
- Early childhood caries risk prediction using machine learning approaches in Bangladesh.BMC oral health · 2025Article
- Tech Bytes-Harnessing Artificial Intelligence for Pediatric Oral Health: A Scoping Review.International journal of clinical pediatric dentistry · 2024Article
- The Use of Artificial Intelligence in Caries Detection: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Artificial Intelligence (AI) Assessment of Pediatric Dental Panoramic Radiographs (DPRs): A Clinical Study.Pediatric reports · 2024Article
- Review
- The Implications of Artificial Intelligence in Pedodontics: A Scoping Review of Evidence-Based Literature.Healthcare (Basel, Switzerland) · 2024Article
- Predicting dental caries outcomes in young adults using machine learning approach.BMC oral health · 2024Article
- Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review.Bioengineering (Basel, Switzerland) · 2024Review
- Utilization of machine learning models in predicting caries risk groups and oral health-related risk factors in adults.BMC oral health · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we developed machine learning-based prediction models for early childhood caries and compared their performances with the traditional regression model. We analyzed the data of 4195 children aged 1-5 years from the Korea National Health and Nutrition Examination Survey data (2007-2018). Moreover, we developed prediction models using the XGBoost (version 1.3.1), random forest, and LightGBM (version 3.1.1) algorithms in addition to logistic regression. Two different methods were applied for variable selection, including a regression-based backward elimination and a random forest-based permutation importance classifier. We compared the area under the receiver operating characteristic (AUROC) values and misclassification rates of the different models and observed that all four prediction models had AUROC values ranging between 0.774 and 0.785. Furthermore, no significant difference was observed between the AUROC values of the four models. Based on the results, we can confirm that both traditional logistic regression and ML-based models can show favorable performance and can be used to predict early childhood caries, identify ECC high-risk groups, and implement active preventive treatments. However, further research is essential to improving the performance of the prediction model using recent methods, such as deep learning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.