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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 patien...

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Autores principales: Alcalá-Rmz, Vanessa, Zanella-Calzada, Laura A., Galván-Tejada, Carlos E., García-Hernández, Alejandra, Cruz, Miguel, Valladares-Salgado, Adan, Galván-Tejada, Jorge I., Gamboa-Rosales, Hamurabi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6388177/
https://www.ncbi.nlm.nih.gov/pubmed/30700010
http://dx.doi.org/10.3390/ijerph16030381
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author Alcalá-Rmz, Vanessa
Zanella-Calzada, Laura A.
Galván-Tejada, Carlos E.
García-Hernández, Alejandra
Cruz, Miguel
Valladares-Salgado, Adan
Galván-Tejada, Jorge I.
Gamboa-Rosales, Hamurabi
author_facet Alcalá-Rmz, Vanessa
Zanella-Calzada, Laura A.
Galván-Tejada, Carlos E.
García-Hernández, Alejandra
Cruz, Miguel
Valladares-Salgado, Adan
Galván-Tejada, Jorge I.
Gamboa-Rosales, Hamurabi
author_sort Alcalá-Rmz, Vanessa
collection PubMed
description 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.
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spelling pubmed-63881772019-02-27 Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks Alcalá-Rmz, Vanessa Zanella-Calzada, Laura A. Galván-Tejada, Carlos E. García-Hernández, Alejandra Cruz, Miguel Valladares-Salgado, Adan Galván-Tejada, Jorge I. Gamboa-Rosales, Hamurabi Int J Environ Res Public Health Article 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. MDPI 2019-01-29 2019-02 /pmc/articles/PMC6388177/ /pubmed/30700010 http://dx.doi.org/10.3390/ijerph16030381 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alcalá-Rmz, Vanessa
Zanella-Calzada, Laura A.
Galván-Tejada, Carlos E.
García-Hernández, Alejandra
Cruz, Miguel
Valladares-Salgado, Adan
Galván-Tejada, Jorge I.
Gamboa-Rosales, Hamurabi
Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks
title Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks
title_full Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks
title_fullStr Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks
title_full_unstemmed Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks
title_short Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks
title_sort identification of diabetic patients through clinical and para-clinical features in mexico: an approach using deep neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6388177/
https://www.ncbi.nlm.nih.gov/pubmed/30700010
http://dx.doi.org/10.3390/ijerph16030381
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