Dental Caries Prediction Based on a Survey of the Oral Health Epidemiology among the Geriatric Residents of Liaoning, China

Abstract

Background. Dental caries is one of the most common chronic diseases observed in elderly patients. The development of preventive strategies for dental caries in elderly individuals is vital. Objective. The objective of the present study was to construct a generalized regression neural network (GRNN) prediction model for the risk assessment of dental caries among the geriatric residents of Liaoning, China. Methods. A stratified equal-capacity random sampling method was used to randomly select 1144 elderly (65-74 years) residents (gender ratio 1 : 1) of Liaoning, China. Data for the oral assessment, including caries characteristics, and questionnaire survey from each participant were collected. Multivariate logistic regression analysis was then performed to identify the independent predictors. GRNN was applied to establish a prediction model for dental caries. The accuracy of the unconditional logistic regression and the GRNN early warning model was compared. Results. A total of 1144 patients fulfilled the requirements and completed the questionnaires. The caries rate was 68.5%, and the main associated factors were toothache history, residence area, smoking, and drinking. We randomly divided the data for the 1144 participants into a training set (915 cases) and a test set (229 cases). The optimal smoothing factor was 0.7, and the area under the receiver operating characteristic curve for the GRNN model was 0.626 (95% confidence interval, 0.544 to 0.708), with a P value of 0.002. In terms of consistency, sensitivity, and specificity, the GRNN model was better than the traditional unconditional multivariate logistic regression model. Conclusion. Geriatric (65-74 years) residents of Liaoning, China, have a high rate of dental caries. Residents with a history of toothache and smoking habits are more susceptible to the disease. The GRNN early warning model is an accurate and meaningful tool for screening, early diagnosis, and treatment planning for geriatric individuals with a high risk of caries.

Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions

“…Therefore, we speculate that the GRNN model has better performance than the LR model for the prognostic prediction for acute stroke. Similar results have been reported by Liu et al [ 14 ], who reported that GRNN models may have better predictive ability than traditional multivariate LR models for dental caries based on the outcomes of an oral health epidemiology survey. Currently, GRNN models have shown use in predicting obstructive sleep apnea [ 27 ], quantitative structure–pharmacokinetic relationship properties of chemical agents [ 28 ], and so on.…”

“…(3) GRNN is similar in principle to neurons of the human brain; it does not have difficulties related to the use of mathematics, and it is likely to make accurate predictions [ 32 ]. However, GRNN is considered a “black box”, which makes it difficult to determine its processes and how it arrives at a prediction or explain the disease-related variables in the predictive model [ 14 ]. LR, which is a commonly used medical statistical tool, also has its irreplaceable advantages, especially in the interpretability and simplicity of finding independent parameters associated with the prognosis of stroke patients [ 8 , 33 ].…”

“…ANN simulates the problem-solving process of the human brain by applying knowledge gained from experience to build a system of "neurons" that can make new decisions, classifications, and predictions [ 9 ]. At present, four main neural network models are used in clinical practice: the back-propagation neural network (BPNN), generalized regression neural network (GRNN), fuzzy neural network, and probabilistic neural network [ 14 ]. The BPNN has been widely used [ 15 ] but suffers from some limitations, such as slow convergence speed, a habit of falling into a local optimal solution, and difficulties with setting an optimal configuration [ 16 ].…”

Clinical Oral Condition Analysis and the Influence of Highly Active Antiretroviral Therapy on Human Salivary Microbial Community Diversity in HIV-Infected/AIDS Patients

“…Non-carious teeth: complete teeth without fillings and without filling treatment. Caries: sulcus or smooth surface of the tooth with a softened lesion at the base, and potential damage to the enamel or softening of the sulcus wall ( Liu et al., 2020 ). The dental examination included number of permanent sound teeth, number of missing teeth from oral disease, number of decayed, missing, filled teeth (DMFT), and Number of decayed, missing, filled surfaces (DMFS).…”

MMDCP: Multi-Modal Dental Caries Prediction for Decision Support System Using Deep Learning

“…Out of the algorithms used, SVM generated the highest performance during evaluation. Liu, L. et al [ 13 ] constructed a generalized regression neural network (GRNN) model to predict caries among residents of Liaoning, China. Using regression on some highly correlated data tends to cause spurious results and overfitting [ 14 ].…”