Development of a recommender system for dental care using machine learning

Abstract

Resource mismanagement along with the underutilization of dental care has led to serious health and economic consequences. Artificial intelligence was applied to a national health database to develop recommendations for dental care. The data were obtained from the 2013-2014 National Health and Nutrition Examination Survey to perform machine learning. Feature selection was done using LASSO in R to determine the best regression model. Prediction models were developed using several supervised machine learning algorithms, including logistic regression, support vector machine, random forest, and classification and regression tree. Feature selection by LASSO along with the inclusion of additional clinically relevant variables identified 8 top features associated with recommendation for dental care. The top 3 features include gum health, number of prescription medications taken, and race. Gum health shows a significantly higher relative importance compared to other features. Demographics, healthcare access, and general health variables were identified as top features related to receiving additional dental care, consistent with prior research. Practicing dentists and other healthcare professionals can follow this model to enable precision dentistry through the incorporation of our algorithms into computerized screening tool or decision tree diagram to achieve more efficient and personalized preventive strategies and treatment protocols in dental care.

Diagnosis of Tooth Prognosis Using Artificial Intelligence

“…In the future, real-time online clinical decision support tools can be created by incorporating the machine-learning algorithms developed from this study to facilitate precision medicine in oral care. According to Hung et al, these algorithms can be used as a screening tool in general medical practices, dental clinics, and social service centers or placed online, providing recommendations for dental examinations for those identified as high risk [ 38 ].…”

A brief exploration of artificial intelligence in dental healthcare: a narrative review

“…39 When applied to assess patient demographic, nutritional, lifestyle, and clinical data, SVM performed at its peak level. 40 Predictive models based on ML methods like SVM, RF, and k-nearest neighbours were used to pinpoint those who are more prone to develop dental surface loss and root cavities.…”

Applications of artificial intelligence in dentistry: A comprehensive review

“…130 Interestingly, the use of DNA data from mucosal microbiome was explored for this task in combination of a RF, and heeded great results. 131 Predicting the need for dental care was also studied using clinical features in combination with a regression model with LASSO feature selection 147 and other methods. 148,149 For dental care prediction, eight features were selected, and the most relevant were the following: gingival health, demographics, healthcare access, and general health variables.…”

“…Predicting the need for dental care was also studied using clinical features in combination with a regression model with LASSO feature selection 147 and other methods 148,149 . For dental care prediction, eight features were selected, and the most relevant were the following: gingival health, demographics, healthcare access, and general health variables 147 . These variables were used as input for different models, such as logistic regression, SVM, RF, and classification and regression tree.…”

“…148,149 For dental care prediction, eight features were selected, and the most relevant were the following: gingival health, demographics, healthcare access, and general health variables. 147 These variables were used as input for different models, such as logistic regression, SVM, RF, and classification and regression tree. RF outperformed the other models in terms of accuracy (84.1%).…”