Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms

Park, Yu Hyun; Kim, Sung Hwa; Choi, Yoon Young

doi:10.3390/ijerph18168613

Cited by 27 publications

(34 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The researcher yielded an AUROC of 0.74, a sensitivity of 0.67, and a positive predictive value of 0.64. Park Y.H et al [24] obtained AUROC values of LR 0.784, XGBoost 0.785, RF 0.780, and LightGBM 0.780 in a study of children under the age of 5. The results of the proposed study are superior to the results of Karhade, Deepti S. et al [22] and Park Y.H et al [24].…”

Section: Discussionmentioning

confidence: 99%

“…Park Y.H et al [24] obtained AUROC values of LR 0.784, XGBoost 0.785, RF 0.780, and LightGBM 0.780 in a study of children under the age of 5. The results of the proposed study are superior to the results of Karhade, Deepti S. et al [22] and Park Y.H et al [24]. For AUROC, our model result, showed RF 0.96, GBDT 0.95, LR 0.89, SVM 0.90, and LSTM 0.83.…”

Section: Discussionmentioning

confidence: 99%

“…You-Hyun Park et. al [24] researched a predictive model for caries in infancy utilisiang logistic regression, XGBoost (version 1.…”

Section: Case Of Caries Calssification Using Survey Datamentioning

confidence: 99%

See 2 more Smart Citations

Optimal Feature Selection based Dental Caries Prediction Model Using Machine Learning for Decision Support System

Kang¹,

Kim²

2023

Preprint

View full text Add to dashboard Cite

Caries is a prevalent oral disease that primarily affects children and teenagers. Advances in ma-chine learning have caught the attention of scientists working with decision support systems to predict early tooth decay. Current research has developed machine learning algorithm for caries classification and reached high accuracy especially in ML for image data. Unfortunately, most studies on dental caries only focus on classification and prediction tasks, meanwhile dental carries prevention is more important. Therefore, this study aims to design an efficient feature for decision support system machine learning based that can identify various risk factors that cause dental caries and its prevention. The data used in the research work was obtained from the 2018 Korean Children's Oral Health Survey, which totaled nine datasets. The experimental results show that combining the mRMR and GINI Feature Importance methods when training with the GBDT model achieved the optimum performance of 95%, 93%, 99%, and 88% for accuracy, F1 score, precision, and recall, respectively. So, the proposed method has provided effective predictive model for dental caries prediction.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Feature Selection based Dental Caries Prediction Model Using Machine Learning for Decision Support System

Kang¹,

Kim²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Articles without complete texts, narrative reviews, scoping reviews, letters to the editor, opinion letters, case reports, brief communications, conference proceedings, and non-English language articles (84 articles) were all omitted ( Figure 1 ). Finally, only 25 papers met the qualifying requirements [ 3 , 7 , 10 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ].…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence Its Uses and Application in Pediatric Dentistry: A Review

et al. 2023

View full text Add to dashboard Cite

In the global epidemic era, oral problems significantly impact a major population of children. The key to a child’s optimal health is early diagnosis, prevention, and treatment of these disorders. In recent years, the field of artificial intelligence (AI) has seen tremendous pace and progress. As a result, AI’s infiltration is witnessed even in those areas that were traditionally thought to be best left to human specialists. The ultimate ability to improve patient care and make precise diagnoses of illnesses has revolutionized the world of healthcare. In the field of dentistry, the competence to execute treatment measures while still providing appropriate patient behavior counseling is in high demand, particularly in the field of pediatric dental care. As a result, we decided to conduct this review specifically to examine the applications of AI models in pediatric dentistry. A comprehensive search of the subjects was done using a wide range of databases to look for studies that have been published in peer-reviewed journals from its inception until 31 December 2022. After the application of the criteria, only 25 of the 351 articles were taken into consideration for this review. According to the literature, AI is frequently used in pediatric dentistry for the purpose of making an accurate diagnosis and assisting clinicians, dentists, and pediatric dentists in clinical decision making, developing preventive strategies, and establishing an appropriate treatment plan.

show abstract

“…Considering this gap in the literature, there is a need to perform large-scale epidemiological research on the association between BMI and dental caries that employs a rigorous protocol regarding BMI cut-off definitions as well as caries assessment including mandatory radiographs in addition to visual inspection and considering the existence of many possible confounders and effect modifiers such as socio-demographics, health-related habits, and metabolic morbidities. While most studies used only statistical models to address the subject, recently, machine learning (ML) approaches in artificial intelligence were used to select the most relevant variables (aka feature selection/feature importance) in identifying root caries [ 25 ] and early childhood caries [ 26 ] using various machine learning as support vector machine, XGBoost and Random Forest [ 25 ], Light Gradient Boosted Machine, logistic regression (including regression-based backward elimination) [ 26 ]. To the best of our knowledge employment of statistical as well as ML models, in the context of BMI categories, cardiometabolic risk factors, and dental caries were not published yet in the English literature.…”

Section: Introductionmentioning

confidence: 99%

Body Mass Index and Caries: Machine Learning and Statistical Analytics of the Dental, Oral, Medical Epidemiological (DOME) Nationwide Big Data Study

et al. 2022

View full text Add to dashboard Cite

The objectives of the research were to analyze the association between Body Mass Index (BMI) and dental caries using novel approaches of both statistical and machine learning (ML) models while adjusting for cardiovascular risk factors and metabolic syndrome (MetS) components, consequences, and related conditions. This research is a data-driven analysis of the Dental, Oral, Medical Epidemiological (DOME) big data repository, that integrates comprehensive socio-demographic, medical, and dental databases of a nationwide sample of dental attendees to military dental clinics for 1 year aged 18–50 years. Obesity categories were defined according to the World Health Organization (WHO): under-weight: BMI < 18.5 kg/m2, normal weight: BMI 18.5 to 24.9 kg/m2, overweight: BMI 25 to 29.9 kg/m2, and obesity: BMI ≥ 30 kg/m2. General linear models were used with the mean number of decayed teeth as the dependent variable across BMI categories, adjusted for (1) socio-demographics, (2) health-related habits, and (3) each of the diseases comprising the MetS definition MetS and long-term sequelae as well as associated illnesses, such as hypertension, diabetes, hyperlipidemia, cardiovascular disease, obstructive sleep apnea (OSA) and non-alcoholic fatty liver disease (NAFLD). After the statistical analysis, we run the XGBoost machine learning algorithm on the same set of clinical features to explore the features’ importance according to the dichotomous target variable of decayed teeth as well as the obesity category. The study included 66,790 subjects with a mean age of 22.8 ± 7.1. The mean BMI score was 24.2 ± 4.3 kg/m2. The distribution of BMI categories: underweight (3113 subjects, 4.7%), normal weight (38,924 subjects, 59.2%), overweight (16,966, 25.8%), and obesity (6736, 10.2%). Compared to normal weight (2.02 ± 2.79), the number of decayed teeth was statistically significantly higher in subjects with obesity [2.40 ± 3.00; OR = 1.46 (1.35–1.57)], underweight [2.36 ± 3.04; OR = 1.40 (1.26–1.56)] and overweight [2.08 ± 2.76, OR = 1.05 (1.01–1.11)]. Following adjustment, the associations persisted for obesity [OR = 1.56 (1.39–1.76)] and underweight [OR = 1.29 (1.16–1.45)], but not for overweight [OR = 1.11 (1.05–1.17)]. Features important according to the XGBoost model were socioeconomic status, teeth brushing, birth country, and sweetened beverage consumption, which are well-known risk factors of caries. Among those variables was also our main theory independent variable: BMI categories. We also performed clinical features importance based on XGBoost with obesity set as the target variable and received an AUC of 0.702, and accuracy of 0.896, which are considered excellent discrimination, and the major features that are increasing the risk of obesity there were: hypertension, NAFLD, SES, smoking, teeth brushing, age as well as our main theory dependent variable: caries as a dichotomized variable (Yes/no). The study demonstrates a positive association between underweight and obesity BMI categories and caries, independent of the socio-demographic, health-related practices, and other systemic conditions related to MetS that were studied. Better allocation of resources is recommended, focusing on populations underweight and obese in need of dental care.

show abstract

Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms

Cited by 27 publications

References 41 publications

Optimal Feature Selection based Dental Caries Prediction Model Using Machine Learning for Decision Support System

Optimal Feature Selection based Dental Caries Prediction Model Using Machine Learning for Decision Support System

Artificial Intelligence Its Uses and Application in Pediatric Dentistry: A Review

Body Mass Index and Caries: Machine Learning and Statistical Analytics of the Dental, Oral, Medical Epidemiological (DOME) Nationwide Big Data Study

Contact Info

Product

Resources

About