Evaluation of Linked Determinants Between Diabetes and Caries

Dental caries and diabetes mellitus, represent two chronic diseases of public health that present statistics of high prevalence worldwide. One of the main problems that affect these conditions is demography, since the least developed countries are those that have less access to public health services and are limited by the economic costs that represent the treatments. In this work is presented the univariate analysis, of a set of demographic determinants obtained from the National Health and Nutrition Examination Surveys (NHANES), in order to identify those that present the most significant information related to estimation of these diseases. According to the results, the age of the subjects is the main determinant describing the simultaneous presence of these diseases, obtaining an area under the curve (AUC) value ≥ 0.846, concluding that age can be used as tool for their preventive diagnosis

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