Dental Caries Risk Assessment in Children 5 Years Old and under via Machine Learning
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 intellig...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Dentistry Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-6767/10/9/164 |
| _version_ | 1797489549844152320 |
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| author | Seyed-Ali Sadegh-Zadeh Ali Rahmani Qeranqayeh Elhadj Benkhalifa David Dyke Lynda Taylor Mahshid Bagheri |
| author_facet | Seyed-Ali Sadegh-Zadeh Ali Rahmani Qeranqayeh Elhadj Benkhalifa David Dyke Lynda Taylor Mahshid Bagheri |
| author_sort | Seyed-Ali Sadegh-Zadeh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T00:19:12Z |
| format | Article |
| id | doaj.art-aeb15794d74a4826ac14b71a3bfcbb33 |
| institution | Directory Open Access Journal |
| issn | 2304-6767 |
| language | English |
| last_indexed | 2024-03-10T00:19:12Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Dentistry Journal |
| spelling | doaj.art-aeb15794d74a4826ac14b71a3bfcbb332023-11-23T15:47:04ZengMDPI AGDentistry Journal2304-67672022-09-0110916410.3390/dj10090164Dental Caries Risk Assessment in Children 5 Years Old and under via Machine LearningSeyed-Ali Sadegh-Zadeh0Ali Rahmani Qeranqayeh1Elhadj Benkhalifa2David Dyke3Lynda Taylor4Mahshid Bagheri5Department of Computing, Staffordshire University, Stoke-on-Trent ST4 2DE, UKDepartment of Computing, Staffordshire University, Stoke-on-Trent ST4 2DE, UKDepartment of Computing, Staffordshire University, Stoke-on-Trent ST4 2DE, UKDepartment of Computing, Staffordshire University, Stoke-on-Trent ST4 2DE, UKDepartment of Computing, Staffordshire University, Stoke-on-Trent ST4 2DE, UKDepartment of Computing, Staffordshire University, Stoke-on-Trent ST4 2DE, UKBackground: 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.https://www.mdpi.com/2304-6767/10/9/164caries predictiondental medicinedental cariesartificial intelligencediagnostic prediction |
| spellingShingle | Seyed-Ali Sadegh-Zadeh Ali Rahmani Qeranqayeh Elhadj Benkhalifa David Dyke Lynda Taylor Mahshid Bagheri Dental Caries Risk Assessment in Children 5 Years Old and under via Machine Learning Dentistry Journal caries prediction dental medicine dental caries artificial intelligence diagnostic prediction |
| title | Dental Caries Risk Assessment in Children 5 Years Old and under via Machine Learning |
| title_full | Dental Caries Risk Assessment in Children 5 Years Old and under via Machine Learning |
| title_fullStr | Dental Caries Risk Assessment in Children 5 Years Old and under via Machine Learning |
| title_full_unstemmed | Dental Caries Risk Assessment in Children 5 Years Old and under via Machine Learning |
| title_short | Dental Caries Risk Assessment in Children 5 Years Old and under via Machine Learning |
| title_sort | dental caries risk assessment in children 5 years old and under via machine learning |
| topic | caries prediction dental medicine dental caries artificial intelligence diagnostic prediction |
| url | https://www.mdpi.com/2304-6767/10/9/164 |
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