دورية أكاديمية
Machine learning to predict untreated dental caries in adolescents.
| العنوان: | Machine learning to predict untreated dental caries in adolescents. |
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| المؤلفون: | Bomfim RA; School of Dentistry, Federal University of Mato Grosso do Sul, Campo Grande, Brazil. aiello.rafael@gmail.com. |
| المصدر: | BMC oral health [BMC Oral Health] 2024 Mar 09; Vol. 24 (1), pp. 316. Date of Electronic Publication: 2024 Mar 09. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: BioMed Central Country of Publication: England NLM ID: 101088684 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6831 (Electronic) Linking ISSN: 14726831 NLM ISO Abbreviation: BMC Oral Health Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: London : BioMed Central, [2001- |
| مواضيع طبية MeSH: | Dental Caries*/epidemiology , Dental Caries*/diagnosis, Humans ; Adolescent ; Artificial Intelligence ; Toothbrushing ; Surveys and Questionnaires ; Machine Learning |
| مستخلص: | Objective: This study aimed to predict adolescents with untreated dental caries through a machine-learning approach using three different algorithms METHODS: Data came from an epidemiological survey in the five largest cities in Mato Grosso do Sul, Brazil. Data on sociodemographic characteristics, consumption of unhealthy foods and behaviours (use of dental floss and toothbrushing) were collected using Sisson's theoretical model, in 615 adolescents. For the machine learning, three different algorithms were used: (1) XGboost; (2) decision tree and (3) logistic regression. The epidemiological baseline was used to train and test predictions to detect individuals with untreated dental caries, through eight main predictor variables. Analyzes were performed using the R software (R Foundation for Statistical Computing, Vienna, Austria). The Ethics Committee approved the study.. Results: For the 615 adolescents, xgboost performed better with an area under the curve (AUC) of 84% versus 81% for the decision tree algorithm. The most important variables were the use of dental floss, unhealthy food consumption, self-declared race and exposure to fluoridated water. Conclusions: Family health teams can improve the work process and use artificial intelligence mechanisms to predict adolescents with untreated dental caries, and, in this way, schedule dental appointments for the treatment of adolescents earlier. (© 2024. The Author(s).) |
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| فهرسة مساهمة: | Keywords: Adolescents; Dental caries; Machine learning; Primary health care contribution statement |
| تواريخ الأحداث: | Date Created: 20240309 Date Completed: 20240311 Latest Revision: 20240312 |
| رمز التحديث: | 20240312 |
| مُعرف محوري في PubMed: | PMC10924973 |
| DOI: | 10.1186/s12903-024-04073-4 |
| PMID: | 38461227 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 1472-6831 |
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| DOI: | 10.1186/s12903-024-04073-4 |