One of the principal conditions that affects oral health worldwide is dental caries, occurring in about 90% of the global population. This pathology has been considered a challenge because of its high prevalence, besides being a chronic but preventable disease which can be caused by a series of different demographic, dietary" among others. Based on this problem, in this research a demographic and dietary features analysis is performed for the classification of subjects according to their oral health status based on caries, according to the age group where the population belongs, using as feature selector a technique based on fast backward selection (FBS) approach for the development of three predictive models, one for each age range (group 1: 10⁻19; group 2: 20⁻59; group 3: 60 or more years old). As validation, a net reclassification improvement (NRI), AUC, ROC, and OR values are used to evaluate their classification accuracy. We analyzed 189 demographic and dietary features from National Health and Nutrition Examination Survey (NHANES) 2013⁻2014. Each model obtained statistically significant results for most features and narrow OR confidence intervals. Age group 2 obtained a mean NRI = -0.080 and AUC = 0.933; age group 3 obtained a mean NRI = -0.024 and AUC = 0.787; and age group 4 obtained a mean NRI = -0.129 and AUC = 0.735. Based on these results, it is concluded that these specific demographic and dietary features are significant determinants for estimating the oral health status in patients based on their likelihood of developing caries, and the age group could imply different risk factors for subjects.
Keywords: NHANES; computer-aided diagnosis; dental caries; fast backward variable selection; net reclassification improvement; predictive multivariate models; statistical analysis.