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Optimizing Clinical Diabetes Diagnosis through Generative Adversarial Networks: Evaluation and Validation

The escalating prevalence of Type 2 Diabetes (T2D) represents a substantial burden on global healthcare systems, especially in regions such as Mexico. Existing diagnostic techniques, although effective, often require invasive procedures and labor-intensive efforts. The promise of artificial intellig...

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Autores principales: García-Domínguez, Antonio, Galván-Tejada, Carlos E., Magallanes-Quintanar, Rafael, Cruz, Miguel, Gonzalez-Curiel, Irma, Delgado-Contreras, J. Rubén, Soto-Murillo, Manuel A., Celaya-Padilla, José M., Galván-Tejada, Jorge I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10594466/
https://www.ncbi.nlm.nih.gov/pubmed/37873778
http://dx.doi.org/10.3390/diseases11040134
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author García-Domínguez, Antonio
Galván-Tejada, Carlos E.
Magallanes-Quintanar, Rafael
Cruz, Miguel
Gonzalez-Curiel, Irma
Delgado-Contreras, J. Rubén
Soto-Murillo, Manuel A.
Celaya-Padilla, José M.
Galván-Tejada, Jorge I.
author_facet García-Domínguez, Antonio
Galván-Tejada, Carlos E.
Magallanes-Quintanar, Rafael
Cruz, Miguel
Gonzalez-Curiel, Irma
Delgado-Contreras, J. Rubén
Soto-Murillo, Manuel A.
Celaya-Padilla, José M.
Galván-Tejada, Jorge I.
author_sort García-Domínguez, Antonio
collection PubMed
description The escalating prevalence of Type 2 Diabetes (T2D) represents a substantial burden on global healthcare systems, especially in regions such as Mexico. Existing diagnostic techniques, although effective, often require invasive procedures and labor-intensive efforts. The promise of artificial intelligence and data science for streamlining and enhancing T2D diagnosis is well-recognized; however, these advancements are frequently constrained by the limited availability of comprehensive patient datasets. To mitigate this challenge, the present study investigated the efficacy of Generative Adversarial Networks (GANs) for augmenting existing T2D patient data, with a focus on a Mexican cohort. The researchers utilized a dataset of 1019 Mexican nationals, divided into 499 non-diabetic controls and 520 diabetic cases. GANs were applied to create synthetic patient profiles, which were subsequently used to train a Random Forest (RF) classification model. The study’s findings revealed a notable improvement in the model’s diagnostic accuracy, validating the utility of GAN-based data augmentation in a clinical context. The results bear significant implications for enhancing the robustness and reliability of Machine Learning tools in T2D diagnosis and management, offering a pathway toward more timely and effective patient care.
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spelling pubmed-105944662023-10-25 Optimizing Clinical Diabetes Diagnosis through Generative Adversarial Networks: Evaluation and Validation García-Domínguez, Antonio Galván-Tejada, Carlos E. Magallanes-Quintanar, Rafael Cruz, Miguel Gonzalez-Curiel, Irma Delgado-Contreras, J. Rubén Soto-Murillo, Manuel A. Celaya-Padilla, José M. Galván-Tejada, Jorge I. Diseases Article The escalating prevalence of Type 2 Diabetes (T2D) represents a substantial burden on global healthcare systems, especially in regions such as Mexico. Existing diagnostic techniques, although effective, often require invasive procedures and labor-intensive efforts. The promise of artificial intelligence and data science for streamlining and enhancing T2D diagnosis is well-recognized; however, these advancements are frequently constrained by the limited availability of comprehensive patient datasets. To mitigate this challenge, the present study investigated the efficacy of Generative Adversarial Networks (GANs) for augmenting existing T2D patient data, with a focus on a Mexican cohort. The researchers utilized a dataset of 1019 Mexican nationals, divided into 499 non-diabetic controls and 520 diabetic cases. GANs were applied to create synthetic patient profiles, which were subsequently used to train a Random Forest (RF) classification model. The study’s findings revealed a notable improvement in the model’s diagnostic accuracy, validating the utility of GAN-based data augmentation in a clinical context. The results bear significant implications for enhancing the robustness and reliability of Machine Learning tools in T2D diagnosis and management, offering a pathway toward more timely and effective patient care. MDPI 2023-09-30 /pmc/articles/PMC10594466/ /pubmed/37873778 http://dx.doi.org/10.3390/diseases11040134 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
García-Domínguez, Antonio
Galván-Tejada, Carlos E.
Magallanes-Quintanar, Rafael
Cruz, Miguel
Gonzalez-Curiel, Irma
Delgado-Contreras, J. Rubén
Soto-Murillo, Manuel A.
Celaya-Padilla, José M.
Galván-Tejada, Jorge I.
Optimizing Clinical Diabetes Diagnosis through Generative Adversarial Networks: Evaluation and Validation
title Optimizing Clinical Diabetes Diagnosis through Generative Adversarial Networks: Evaluation and Validation
title_full Optimizing Clinical Diabetes Diagnosis through Generative Adversarial Networks: Evaluation and Validation
title_fullStr Optimizing Clinical Diabetes Diagnosis through Generative Adversarial Networks: Evaluation and Validation
title_full_unstemmed Optimizing Clinical Diabetes Diagnosis through Generative Adversarial Networks: Evaluation and Validation
title_short Optimizing Clinical Diabetes Diagnosis through Generative Adversarial Networks: Evaluation and Validation
title_sort optimizing clinical diabetes diagnosis through generative adversarial networks: evaluation and validation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10594466/
https://www.ncbi.nlm.nih.gov/pubmed/37873778
http://dx.doi.org/10.3390/diseases11040134
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