Artificial Intelligence in Health Data Analysis: The Darwinian Evolution Theory Suggests an Extremely Simple and Zero-Cost Large-Scale Screening Tool For Prediabetes and Type 2 Diabetes
Open accessAbstract
Aims The effective identification of individuals with early dysglycemia status is key to reduce the incidence of type 2 diabetes. We develop and validate a novel zero-cost tool that significantly simplifies the screening of undiagnosed dysglycemia. Methods We use NHANES cross-sectional data over 10 years (2007-2016) to derive an equation that links non-laboratory exposure variables to the possible presence of undetected dysglycemia. For the first time, we adopt a novel artificial intelligence approach based on the Darwinian evolutionary theory to analyze health data. We collected data for 47 variables. Results Age and waist circumference are the only variables required to use the model. To identify undetected dysglycemia, we obtain an area under the curve (AUC) of 75.3%. Sensitivity and specificity are 0.65 and 0.73 by using the optimal threshold value determined from external validation data. Conclusions The use of uniquely two variables allows to obtain a zero-cost screening tool of analogous precision than that of more complex tools widely adopted in the literature. The newly developed tool has clinical use as it significantly simplifies the screening of dysglycemia. Furthermore, we suggest that the definition of an age-related waist circumference cut-off might help to improve existing diabetes risk factors.
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Anthropometric measures, particularly those estimating abdominal obesity such as waist circumference, serve as powerful predictors in machine learning models for diabetes risk assessment.
“Recent advances have demonstrated that anthropometric measures, particularly those estimating abdominal obesity such as waist circumference, serve as powerful predictors in machine learning models for diabetes risk assessment (35).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [35]
Buccheri et al. demonstrated a zero-cost screening tool using only age and waist circumference achieved 75.3% area under the curve for detecting undiagnosed dysglycemia, with sensitivity and specificity of 0.65 and 0.73 respectively.
“Remarkably, Buccheri et al. demonstrated that a zero-cost screening tool utilising only two variables, age and waist circumference, could achieve 75.3% area under the curve (AUC) for detecting undiagnosed dysglycemia, with sensitivity and specificity of 0.65 and 0.73 respectively (35).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [35]
The richness of NHANES data has been particularly valuable in identifying novel predictors such as waist circumference as a powerful indicator of diabetes risk.
“The richness of NHANES data, spanning decades of collection and including detailed anthropometric measurements, laboratory results, and lifestyle factors, has been particularly valuable in identifying novel predictors such as waist circumference as a powerful indicator of diabetes risk (35).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [35]
NHANES data has enabled the development of sophisticated machine learning algorithms for prediabetes prediction, with studies demonstrating superior performance compared to traditional screening tools through feature selection techniques.
“NHANES data has enabled the development of sophisticated machine learning algorithms for prediabetes prediction, with studies demonstrating superior performance compared to traditional screening tools through feature selection techniques applied to comprehensive exposure variables (34-36).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [35]
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- Authors
- Buccheri, E., Dell'Aquila, D., & Russo, M.
- Affiliations
- University of Catania (IT)
- Corresponding author
- Buccheri, Enrico
- Venue
- Diabetes Research and Clinical Practice
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 174
- Pages
- 108722
- ISSN
- 0168-8227 (ISSN-L)
- OpenAlex
- W3135607728
- Cited by
- 24
- References
- 51
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 4 · 2023: 7 · 2022: 3 · 2021: 2
- FWCI
- 2.63
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Diabetes Research and Clinical Practice (publishedVersion)
- SSRN Electronic Journal (publishedVersion)
- PubMed (publishedVersion)
- UnissResearch (Università degli Studi di Sassari) (submittedVersion)
- Funders
- Ministero dell’Istruzione, dell’Università e della Ricerca
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- prediabetes, type 2 diabetes, diabetes screening, dysglycemia, Darwinian evolution, artificial intelligence, waist circumference, NHANES, risk prediction model
- MeSH terms
- Data Analysis, Artificial Intelligence, Cross-Sectional Studies, Diabetes Mellitus, Type 2, Female, Humans, Male, Middle Aged, Nutrition Surveys, Prediabetic State, Evolution, Molecular
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