Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks.

Diabetes is a chronic and noncommunicable but preventable disease that is affecting the Mexican population at worrying levels, being the first place in prevalence worldwide. Early diabetes detection has become important to prevent other health conditions that involve low organ yield until the patient death. Based on this problem, this work proposes the architecture of an Artificial Neural Network (ANN) for the automated classification of healthy patients from diabetics patients. The analysis was performed used a set of 19 para-clinical features to determine the health status of the patients. The developed model was evaluated through a statistical analysis based on the calculation of the loss function, accuracy, area under the curve (AUC) and receiving operating characteristics (ROC) curve. The results obtained present statistically significant values, with accuracy of 0.94 and AUC values of 0.98. Based on these results, it is possible to conclude that the ANN implemented in this work can classify patients with presence of diabetes from controls with significant accuracy, presenting preliminary results for the development of a diagnostic tool that can be supportive for health specialists.

A Case⁻Control Study of Socio-Economic and Nutritional Characteristics as Determinants of Dental Caries in Different Age Groups, Considered as Public Health Problem: Data from NHANES 2013⁻2014.

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.

Adolescence metabolic syndrome or adiposity and early adult metabolic syndrome.

OBJECTIVE: To investigate the predictive role of adolescent metabolic syndrome (MetS) in development of early adult MetS, independent of adult body mass index (BMI). STUDY DESIGN: 1424 adolescents (639 boys), participants of the Tehran Lipid and Glucose Study, followed for 10.4 years, were analyzed and logistic regression models were developed. Using the areas under the receiver operating characteristic curve, the discriminatory ability of adolescent MetS and overweight or obesity was evaluated. Net reclassification improvement was calculated to determine the accuracy of classification by adolescent MetS in place of overweight or obesity. RESULTS: The mean ± SD of age and BMI were 14.6 ± 2.2 years and 20.3 ± 4.2 kg/m(2), respectively. The prevalence of MetS was 13.3% and 14.6% at baseline and after follow-up, respectively. The risk of developing early adult MetS among subjects who were overweight or obese in adolescence but nonobese as adults (OR: 1.65) was lower than the risk among subjects who were obese as adults but nonobese as adolescents (OR: 8.45). After adjustment for adult BMI, adolescent MetS and overweight or obesity did not show any association with the risk of adult MetS. Area under the receiver operating characteristic curve was higher for obesity (0.619) than MetS (0.589) and the net reclassification improvement value for MetS was 1.5% (P = .398). CONCLUSION: Adolescent MetS or adiposity did not predict early adult MetS independent of adult BMI. The addition of adolescent MetS to obesity does not improve the predictive power for early adult MetS.