Prediction of cardiovascular complication in patients with newly diagnosed type 2 diabetes using an XGBoost/GRU-ODE-bayes-based machine-learning algorithm
Open accessAbstract
Background: Cardiovascular disease is life-threatening yet preventable for patients with type 2 diabetes mellitus (T2DM). Because each patient with T2DM has a different risk of developing cardiovascular complications, the accurate stratification of cardiovascular risk is critical. In this study, we proposed cardiovascular risk engines based on machine-learning algorithms for newly diagnosed T2DM patients in Korea.Methods: To develop the machine-learning-based cardiovascular disease engines, we retrospectively analyzed 26,166 newly diagnosed T2DM patients who visited Seoul St. Mary’s Hospital between July 2009 and April 2019. To accurately measure diabetes-related cardiovascular events, we designed a buffer (1 year), an observation (1 year), and an outcome period (5 years). The entire dataset was split into training and testing sets in an 8:2 ratio, and this procedure was repeated 100 times. The area under the receiver operating characteristic curve (AUROC) was calculated by 10-fold cross-validation on the training dataset.Results: The machine-learning-based risk engines (AUROC XGBoost=0.781±0.014 and AUROC gated recurrent unit [GRU]-ordinary differential equation [ODE]-Bayes=0.812±0.016) outperformed the conventional regression-based model (AUROC=0.723± 0.036).Conclusion: GRU-ODE-Bayes-based cardiovascular risk engine is highly accurate, easily applicable, and can provide valuable information for the individualized treatment of Korean patients with newly diagnosed T2DM.
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Lee et al. developed a model using South Korean EHR data to predict cardiovascular complications in newly diagnosed T2D patients.
“Lee et al. [92] developed a model using South Korean EHR data to predict cardiovascular complications in newly diagnosed T2D patients”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [92]
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References 29 · Cited by 9
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- Authors
- Lee, J., Choi, Y., Ko, T., Lee, K., Shin, J., & Kim, H.-S.
- Affiliations
- The Catholic University of Korea Seoul St. Mary's Hospital (KR)
- NAVER Cloud (South Korea) (KR)
- Catholic University of Korea (KR)
- Corresponding author
- Kim, Hun-Sung
- Venue
- Endocrinology and Metabolism
- Publisher
- Korean Endocrine Society
- Published
- 2024-02-29
- Volume
- 39
- Issue
- 1
- Pages
- 176-185
- ISSN
- 2093-596X (ISSN-L)
- 2093-5978
- PMC
- PMC10901655
- OpenAlex
- W4388848131
- Cited by
- 9
- References
- 29
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 2
- FWCI
- 2.02
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Endocrinology and Metabolism (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- type 2 diabetes, cardiovascular complications, cardiovascular risk prediction, newly diagnosed diabetes, XGBoost, gated recurrent unit, ordinary differential equations, Bayesian neural networks, risk stratification, Korean patients
- MeSH terms
- Machine Learning, Algorithms, Bayes Theorem, Cardiovascular Diseases, Diabetes Mellitus, Type 2, Humans, Retrospective Studies
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