Abstract：
: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.
journal_name
Int J Environ Res Public Healthauthors
Alcalá-Rmz V,Zanella-Calzada LA,Galván-Tejada CE,García-Hernández A,Cruz M,Valladares-Salgado A,Galván-Tejada JI,Gamboa-Rosales Hdoi
10.3390/ijerph16030381subject
Has Abstractpub_date
2019-01-29 00:00:00issue
3eissn
1661-7827issn
1660-4601pii
ijerph16030381journal_volume
16pub_type
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