ArticleInternational journal of environmental research and public health2019
Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks.
Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Evaluation.Journal of diabetes research · 2023Article
- Machine Learning for Screening Microvascular Complications in Type 2 Diabetic Patients Using Demographic, Clinical, and Laboratory Profiles.Journal of clinical medicine · 2022Article
- Identification of People with Diabetes Treatment through Lipids Profile Using Machine Learning Algorithms.Healthcare (Basel, Switzerland) · 2021Article
- Distal Symmetric Polyneuropathy Identification in Type 2 Diabetes Subjects: A Random Forest Approach.Healthcare (Basel, Switzerland) · 2021Article
- Prediction of metabolic syndrome based on sleep and work-related risk factors using an artificial neural network.BMC endocrine disorders · 2020Article
- Univariate Analysis of Short-Chain Fatty Acids Related to Sudden Infant Death Syndrome.Diagnostics (Basel, Switzerland) · 2020Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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