Leveraging Real-World Data to Predict Macrovascular Complications in Type 2 Diabetes: A Comparative Machine Learning Study
Journal
Lecture Notes in Computer Science
Start Page
201
End Page
206
ISSN
0302-9743
1611-3349
ISBN
9789819688913
9789819688920
Date Issued
2026
Author(s)
Abstract
Worldwide, type 2 diabetes mellitus (T2DM) prevalence is rising, and macrovascular complications remain its primary cause of death. Although glycemic control alleviates microvascular damage, risk factors for macrovascular complications remain unclear. Recent findings indicate that models built on real-world data outperform those based solely on clinical trials in forecasting T2DM outcomes. Using electronic medical records, we constructed XGBoost, decision trees, and random forest models to predict four macrovascular complications in T2DM. XGBoost achieved the highest accuracies—0.985 for hypertensive heart disease, 0.960 for ischemic heart disease, 0.956 for cerebrovascular disease, and 0.969 for coronary heart disease. These results can be incorporated into clinical workflows, streamlining information exchange for healthcare providers and patients.
SDGs
Publisher
Springer Nature Singapore
Type
conference presentation