Endocrinol Metab.  2024 Feb;39(1):176-185. 10.3803/EnM.2023.1739.

Prediction of Cardiovascular Complication in Patients with Newly Diagnosed Type 2 Diabetes Using an XGBoost/GRU-ODE-Bayes-Based Machine-Learning Algorithm

• Lee J
• Choi Y
• Ko T
• Lee K
• Shin J
• Kim HS

• KMID: 2552808
• DOI: http://doi.org/10.3803/EnM.2023.1739

Abstract

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.

Keyword

Cardiovascular diseases; Diabetes mellitus, type 2; Korea; Machine learning

Figure

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  Fig. 1. Study scheme. The study enrolled 26,166 newly diagnosed type 2 diabetes mellitus (T2DM) patients. A total of 5,040 patients were analyzed to establish the model.

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  Fig. 2. Study design. To minimize non-diabetes-related cardiovascular events and maximize the accuracy of the model, observation (1 year), buffer (1 year), and outcome (5 years) periods were included in the study design. Variables for the model were acquired during the observation period and cardiovascular events were measured during the outcome period. Cardiovascular events that developed during the buffer period were excluded. BMI, body mass index.

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  Fig. 3. The eXtreme Gradient Boosting (XGBoost)/gated recurrent unit (GRU)-ordinary differential equation (ODE)-Bayes-based machine- learning algorithm predicts cardiovascular complications in patients with type 2 diabetes mellitus. A receiver operating characteristic curve was drawn based on the machine-learning algorithms (GRU-ODE-Bayes [blue] and XGBoost [red]). The area under the receiver operating characteristic curve (AUROC) was measured (right bottom). The ±1 standard deviation was indicated as grey area in the curve. ROC, receiver operating characteristic.

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