Early detection of type 2 diabetes mellitus using machine learning-based prediction models
Open accessAbstract
Most screening tests for T2DM in use today were developed using multivariate regression methods that are often further simplified to allow transformation into a scoring formula. The increasing volume of electronically collected data opened the opportunity to develop more complex, accurate prediction models that can be continuously updated using machine learning approaches. This study compares machine learning-based prediction models (i.e. Glmnet, RF, XGBoost, LightGBM) to commonly used regression models for prediction of undiagnosed T2DM. The performance in prediction of fasting plasma glucose level was measured using 100 bootstrap iterations in different subsets of data simulating new incoming data in 6-month batches. With 6 months of data available, simple regression model performed with the lowest average RMSE of 0.838, followed by RF (0.842), LightGBM (0.846), Glmnet (0.859) and XGBoost (0.881). When more data were added, Glmnet improved with the highest rate (+ 3.4%). The highest level of variable selection stability over time was observed with LightGBM models. Our results show no clinically relevant improvement when more sophisticated prediction models were used. Since higher stability of selected variables over time contributes to simpler interpretation of the models, interpretability and model calibration should also be considered in development of clinical prediction models.
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AI algorithms can predict the onset of type 2 diabetes and its complications with remarkable accuracy.
“Various studies have shown that AI algorithms can predict the onset of T2D and its complications with remarkable accuracy (13-15).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [14]
The Results (Model performance; page unavailable) report AUCs up to 0.859 for predicting undiagnosed T2D, supporting the claim that the models predict diabetes with substantial accuracy. The paper does not report prediction accuracy for complications, and its study concerns detecting undiagnosed disease rather than prospectively predicting onset. (LLM, conf=0.86)
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- Authors
- Kopitar, L., Kocbek, P., Cilar, L., Sheikh, A., & Stiglic, G.
- Affiliations
- University of Primorska (SI)
- University of Maribor (SI)
- Brigham and Women's Hospital (US)
- Corresponding author
- Kopitar, Leon
- Venue
- Scientific Reports
- Published
- 2020-07-20
- Volume
- 10
- Issue
- 1
- Pages
- 11981-11981
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC7371679
- OpenAlex
- W3043363778
- Cited by
- 369
- References
- 50
- Citations by year
- 2026: 37 · 2025: 71 · 2024: 77 · 2023: 80 · 2022: 69 · 2021: 32 · 2020: 3
- FWCI
- 40.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- Funders
- European Commission (2014‐2020, P2-0057)
- Javna Agencija za Raziskovalno Dejavnost RS (N2-0101, P2-0057)
- Univerza v Mariboru
- European Social Fund (20142020)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- type 2 diabetes, early detection, diabetes screening, prediction model, machine learning, fasting blood glucose, regression models, model interpretability, model calibration, LightGBM, XGBoost, random forest, glmnet
- MeSH terms
- Machine Learning, Blood Glucose, Calibration, Diabetes Mellitus, Type 2, Fasting, Female, Humans, Male, Middle Aged, Models, Biological, Area Under Curve, Early Diagnosis
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