Early childhood caries risk prediction using machine learning approaches in Bangladesh
Bibliographic record
Abstract
BACKGROUND: In the last years, artificial intelligence (AI) has contributed to improving healthcare including dentistry. The objective of this study was to develop a machine learning (ML) model for early childhood caries (ECC) prediction by identifying crucial health behaviours within mother-child pairs. METHODS: For the analysis, we utilized a representative sample of 724 mothers with children under six years in Bangladesh. The study utilized both clinical and survey data. ECC was assessed using ICDAS II criteria in the clinical examinations. Recursive Feature Elimination (RFE) and Random Forest (RF) was applied to identify the optimal subsets of features. Random forest classifier (RFC), extreme gradient boosting (XGBoost), support vector machine (SVM), adaptive boosting (AdaBoost), and multi-layer perceptron (MLP) models were used to identify the best fitted model as the predictor of ECC. SHAP and MDG-MDA plots were visualized for model interpretability and identify significant predictors. RESULTS: The RFC model identified 10 features as the most relevant for ECC prediction obtained by RFE feature selection method. The features were: plaque score, age of child, mother's education, number of siblings, age of mother, consumption of sweet, tooth cleaning tools, child's tooth brushing frequency, helping child brushing, and use of F-toothpaste. The final ML model achieved an AUC-ROC score (0.77), accuracy (0.72), sensitivity (0.80) and F1 score (0.73) in the test set. Of the prediction model, dental plaque was the strongest predictor of ECC (MDG: 0.08, MDA: 0.10). CONCLUSIONS: Our final ML model, integrating 10 key features, has the potential to predict ECC effectively in children under five years. Additional research is needed for validation and optimization across various groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".