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Early childhood caries (ECC) prediction models using Machine Learning
Daniel José Blanco-Victorio1, Roxana Patricia López-Ramos2, Johan Daniel Blanco-Rodriguez3
1Facultad de Ciencias e Ingeniería Universidad Peruana Cayetano Heredia Lima. Perú.
Insights
Machine learning models effectively predict early childhood caries (ECC). Support Vector Machine and Neural Networks demonstrated superior performance in identifying children with cavities, aiding in early diagnosis and intervention.
Area of Science:
- Dentistry
- Computer Science
- Public Health
Background:
- Early childhood caries (ECC) is a significant public health concern.
- Predictive models can aid in early diagnosis and prevention strategies.
- Understanding the performance of machine learning for ECC prediction is crucial.
Purpose of the Study:
- To evaluate machine learning models for predicting early childhood caries.
- To compare the performance of different prediction models using key indicators.
Main Methods:
- A cross-sectional study analyzed data from 186 children aged 3-6 years.
- Machine learning models were applied using Orange Data Mining software.
- Model performance was assessed using precision, recall, F1-score, accuracy, and ROC curves.
Main Results:
- 76.88% of children presented with cavities.
- Support Vector Machine (SVM) and Neural Network (NN) models showed the highest performance.
- SVM and NN achieved high accuracy (0.927) and recall (0.974).
Conclusions:
- Machine learning models, particularly SVM and NN, are effective for caries prediction.
- These AI-driven tools show promise for identifying children at risk of ECC.
- Further research can refine these models for clinical application.
Background:
To evaluate the performance of different prediction models based on machine learning to predict the presence of early childhood caries.
Material And Methods:
Cross-sectional analytical study. The sociodemographic and clinical data used came from a sample of 186 children aged 3 to 6 years and their respective parents or guardians treated at a Hospital in Ica, Peru. The database with significant variables was loaded into the Orange Data Mining software to be processed with different prediction models based on Machine Learning. To evaluate the performance of the prediction models, the following indicators were used: precision, recall, F1-score and accuracy. The discriminatory power of the model was determined by the value of the ROC curve.
Results:
76.88% of the children evaluated had cavities. The Support Vector Machine (SVM) and Neural Network (NN) models obtained the best performance values, showing similar values of accuracy, F1-score and recall (0.927, 0.950 and 0.974; respectively). The probability of correctly distinguishing a child with ECC was 90.40% for the SVM model and 86.68% for the NN model.
Conclusions:
The Machine Learning-based caries prediction models with the best performance were Support Vector Machine (SVM) and Neural Networks (NN). Key words:Early childhood caries, Caries prediction, Machine Learning, Artificial intelligence, caries.
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