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Prediction of Cardiovascular Complication in Patients with Newly Diagnosed Type 2 Diabetes Using an XGBoost/GRU-ODE-Bayes-Based Machine-Learning Algorithm.
- Source :
-
Endocrinology and metabolism (Seoul, Korea) [Endocrinol Metab (Seoul)] 2024 Feb; Vol. 39 (1), pp. 176-185. Date of Electronic Publication: 2023 Nov 21. - Publication Year :
- 2024
-
Abstract
- Backgruound: 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.<br />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.<br />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).<br />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.
Details
- Language :
- English
- ISSN :
- 2093-5978
- Volume :
- 39
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Endocrinology and metabolism (Seoul, Korea)
- Publication Type :
- Academic Journal
- Accession number :
- 37989268
- Full Text :
- https://doi.org/10.3803/EnM.2023.1739