Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks
| Autor: | Miguel Cruz, Vanessa Alcalá-Rmz, Hamurabi Gamboa-Rosales, Adan Valladares-Salgado, Laura A. Zanella-Calzada, Carlos E. Galván-Tejada, Alejandra García-Hernández, Jorge I. Galván-Tejada |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Adult
Male Artificial Neural Network medicine.medical_specialty Health Status Health Toxicology and Mutagenesis lcsh:Medicine 030209 endocrinology & metabolism Type 2 diabetes Disease Article 03 medical and health sciences 0302 clinical medicine statistical analysis Diabetes mellitus Diabetes Mellitus medicine Humans Statistical analysis 030212 general & internal medicine Mexico Artificial neural network business.industry lcsh:R Public Health Environmental and Occupational Health Area under the curve Middle Aged Models Theoretical medicine.disease net reclassification improvement Identification (information) Early Diagnosis ROC Curve Computer-aided diagnosis Area Under Curve Emergency medicine Female computer-aided diagnosis Neural Networks Computer type 2 diabetes business |
| Zdroj: | International Journal of Environmental Research and Public Health, Vol 16, Iss 3, p 381 (2019) International Journal of Environmental Research and Public Health Volume 16 Issue 3 |
| ISSN: | 1660-4601 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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