Dental Caries Risk Assessment in Children 5 Years Old and under via Machine Learning

Abstract

Background: Dental caries is a prevalent, complex, chronic illness that is avoidable. Better dental health outcomes are achieved as a result of accurate and early caries risk prediction in children, which also helps to avoid additional expenses and repercussions. In recent years, artificial intelligence (AI) has been employed in the medical field to aid in the diagnosis and treatment of medical diseases. This technology is a critical tool for the early prediction of the risk of developing caries. Aim: Through the development of computational models and the use of machine learning classification techniques, we investigated the potential for dental caries factors and lifestyle among children under the age of five. Design: A total of 780 parents and their children under the age of five made up the sample. To build a classification model with high accuracy to predict caries risk in 0–5-year-old children, ten different machine learning modelling techniques (DT, XGBoost, KNN, LR, MLP, RF, SVM (linear, rbf, poly, sigmoid)) and two assessment methods (Leave-One-Out and K-fold) were utilised. The best classification model for caries risk prediction was chosen by analysing each classification model’s accuracy, specificity, and sensitivity. Results: Machine learning helped with the creation of computer algorithms that could take a variety of parameters into account, as well as the identification of risk factors for childhood caries. The performance of the classifier is almost unbiased, making it generalizable. Among all applied machine learning algorithms, Multilayer Perceptron and Random Forest had the best accuracy, with 97.4%. Support Vector Machine with RBF Kernel (with an accuracy of 97.4%) was better than Extreme Gradient Boosting (with 94.9% accuracy). Conclusion: The outcomes of this study show the potential of regular screening of children for caries risk by experts and finding the risk scores of dental caries for any individual. Therefore, in order to avoid dental caries, it is possible to concentrate on each individual by utilizing machine learning modelling.

Utilization of machine learning models in predicting caries risk groups and oral health-related risk factors in adults

“…It is important to note that the consumption of sugary snacks is a significant risk factor for caries formation, with studies showing that it increases the risk of caries fivefold [ 62 ]. This finding is supported by the findings of other studies in the literature [ 29 , 30 , 32 , 58 ]. Flossing, which is a crucial aspect of oral health, is known to prevent root caries [ 63 ].…”

“…Sadegh-Zadeh et al [ 29 ] sampled a total of 780 parents and children under the age of five to assess the risk of dental caries in children aged 5 years and under. They employed ten different machine learning modeling techniques to build a highly accurate classification model to predict caries risk with this data and showed that RF and MLP machine learning models had the best accuracy of 97.4%.…”

“…Kang et al [ 26 ], Sadegh-Zadeh et al [ 29 ], Elani et al [ 58 ], Yang et al [ 31 ] and Qu et al [ 32 ] found that RF is the machine learning model with the highest success rate. In our study, this model was the second most successful model after the MLP algorithm.…”

Artificial intelligence approaches for tinnitus diagnosis: leveraging high-frequency audiometry data for enhanced clinical predictions

“…Data preprocessing is a crucial step in any machine learning project, as it involves preparing and cleaning the data to ensure that the machine learning algorithms can effectively process and analyse it (Sadegh-Zadeh et al, 2022a , b , 2023a , b ). For this study, focused on diagnosing tinnitus using high-frequency audiometry data, the preprocessing involved several specific steps, particularly due to the complexity of extracting data from audiograms in PDF format.…”

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