Early childhood caries (ECC) prediction models using Machine Learning
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Daniel José Blanco-Victorio [1] ; Roxana Patricia López-Ramos [2] ; Johan Daniel Blanco-Rodriguez [3] ; Nieves Asteria López-Luján [5] ; Gina Fiorella León-Untiveros [4] ; Ana Lucy Siccha-Macassi [4]
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[1] Universidad Peruana Cayetano Heredia
Universidad Peruana Cayetano Heredia
Perú
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[2] Universidad Científica del Sur
Universidad Científica del Sur
Perú
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[3] Universidad de Lima
Universidad de Lima
Perú
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[4] Universidad Nacional del Callao
Universidad Nacional del Callao
Callao, Perú
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[5] Hospital Santa María del Socorro, Ica-Perú
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- Localización: Journal of Clinical and Experimental Dentistry, ISSN-e 1989-5488, Vol. 16, Nº. 12 (December), 2024, págs. 1523-1529
- Idioma: inglés
- Enlaces
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Resumen
To evaluate the performance of different prediction models based on machine learning to predict the presence of early childhood caries.
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.
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.
The Machine Learning-based caries prediction models with the best performance were Support Vector Machine (SVM) and Neural Networks (NN).
