Optimizing Clinical Diabetes Diagnosis through Generative Adversarial Networks: Evaluation and Validation
Open accessAbstract
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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Generative AI models are particularly useful in diabetes care because diabetes and its complications are multifactorial.
“These models are particularly useful in diabetes care because of the multifactorial nature of the disease and its complications (71).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [71]
The Introduction describes diabetes as multifactorial and lists its complications, while Methods §3.2 and Future Work describe generative networks as useful for augmenting diabetes patient data. The paper does not say that the disease’s multifactorial nature or its complications are why generative models are particularly useful; Future Work proposes investigating additional variables with Transformer-based models. (LLM, conf=0.84)
Experimental — accuracy not yet measuredRecent studies have demonstrated the utility of generative AI in detecting early signals of diabetes complications.
“Recent studies done have demonstrated the utility of generative AI in detecting early signals of diabetes complications (49,71,76).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [71]
The Discussion and Conclusions report that GAN-generated data improved a classifier’s performance in identifying people with diabetes. The paper links early detection of diabetes to reducing the risk of complications, but it does not report detecting early signals of the complications themselves. (LLM, conf=0.88)
Experimental — accuracy not yet measured
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- Authors
- García-Domínguez, A., Galván-Tejada, C. E., Magallanes-Quintanar, R., Cruz, M., Gonzalez-Curiel, I., Delgado-Contreras, J. R., Soto-Murillo, M. A., Celaya-Padilla, J. M., & Galván-Tejada, J. I.
- Affiliations
- Universidad Autónoma de Zacatecas "Francisco García Salinas" (MX)
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro, Zacatecas 98000, Mexico
- Mexican Social Security Institute (MX)
- ORCID
- Venue
- Diseases
- Publisher
- MDPI AG
- Published
- 2023-09-30
- Volume
- 11
- Issue
- 4
- Pages
- 134
- ISSN
- 2079-9721 (ISSN-L)
- PMC
- PMC10594466
- OpenAlex
- W4387234568
- Cited by
- 7
- References
- 18
- Citations by year
- 2026: 4 · 2025: 3
- FWCI
- 1.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diseases (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Keywords
- type 2 diabetes, diabetes diagnosis, generative adversarial networks, synthetic data generation, data augmentation, random forest classification, Mexican patients, diagnosis
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