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Published on: June 20, 2020
Developing an Interpretable Machine Learning Framework to Predict and Analyse Early Childhood Caries in Children Aged
Xinyue Yuan1, Yiting Chu1, Wenyan Cai1
1Department of Stomatology, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Insights
Early childhood caries (ECC) is a common issue. Machine learning models identified maternal education, diet, and lifestyle as key predictors, enabling targeted prevention strategies for better child oral health.
Area of Science:
- Pediatric Dentistry
- Public Health
- Machine Learning in Healthcare
Background:
- Early childhood caries (ECC) significantly impacts children's overall health and quality of life globally.
- While numerous risk factors for ECC are known, systematic prioritization of predictors is limited.
- Understanding key determinants is crucial for effective intervention strategies.
Purpose of the Study:
- To identify and prioritize predictors of early childhood caries (ECC) using advanced analytical methods.
- To develop and evaluate machine learning (ML) models for predicting ECC risk in children.
- To enable risk stratification for targeted preventive interventions.
Main Methods:
- An observational study involving 900 children, collecting data on demographics, parental background, feeding, health, hygiene, and diet.
- Univariate and multivariate analyses to identify ECC-associated factors.
- Development and comparison of five ML models (LR, RF, DT, KNN, SVM) for ECC prediction and risk stratification.
Main Results:
- Key predictors identified include age, maternal education, vitamin supplementation, carbohydrate intake, nighttime feeding, sleep light exposure, and outdoor activity.
- Logistic Regression (LR) demonstrated the highest prediction accuracy at 99.4%, followed by SVM (98.9%) and RF (96.7%).
- ML models successfully stratified children into low-, moderate-, and high-risk groups for ECC.
Conclusions:
- Machine learning models effectively pinpointed critical ECC determinants, emphasizing maternal education, diet, and lifestyle.
- Risk stratification based on ML predictions can guide targeted early preventive interventions.
- These findings support reducing the global burden of early childhood caries through informed strategies.
Background:
Early childhood caries (ECC) is a prevalent global oral health condition, exerting profound adverse effects on children's oral health, general health, and quality of life. Its onset involves demographic, socioeconomic, behavioural, and clinical factors. While many studies report individual risks, systematic analyses to prioritise predictors remain limited.
Methods:
This single-centre observational study included 900 children who visited the hospital between October 2022 and May 2024. Collected variables included age, parental background, feeding practices, general health status, oral hygiene and dietary habits. Univariate and multivariate analyses were conducted to identify factors associated with ECC. Five machine learning (ML) models - logistic regression (LR), random forest (RF), decision tree (DT), k-nearest neighbours (KNN) and support vector machine (SVM) - were developed to predict ECC. ECC presence or absence was used as the clinical outcome. Risk stratification classified children into low-, moderate-, and high-risk groups.
Results:
Significant predictors included age, maternal education, vitamin supplementation during pregnancy and for the child, carbohydrate intake, nighttime feeding behaviours, cessation timing, light exposure during sleep and outdoor activity (P < .05). LR achieved the highest accuracy (99.4%), followed by SVM (98.9%), RF (96.7%), DT (95.0%) and KNN (91.1%).
Conclusion:
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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