Type 2 Diabetes Prediction Using Vascular Stiffness Parameters
Problem statement
Type 2 Diabetes Mellitus (T2DM) is a major global health concern associated with severe cardiovascular complications, including increased arterial stiffness. An effective, non-invasive screening method is critical for early risk assessment before complications develop.
My contribution
Comparative Study
Trained and validated four models — Logistic Regression, SVM, Random Forest, and XGBoost — on a dataset of 69 Indonesian subjects (21 diagnosed with diabetes).
Robust Validation
Applied a Leave-One-Out Cross-Validation (LOOCV) strategy to get reliable performance estimates on a small clinical dataset.
Feature Engineering
Used 13 non-invasive predictors spanning demographic/anthropometric (Age, BMI, Vascular Age) and hemodynamic/vascular parameters (ABI, baPWV, Aortic & Peripheral Compliance).
Technical stack
Results & impact
- ✓Random Forest achieved the best performance: 85.5% overall accuracy, 0.921 AUC, and a 0.74 F1-score for the diabetic class.
- ✓Demonstrated that non-invasive vascular parameters are a viable basis for early diabetes screening.
- ✓Positioned the approach as a potential clinical decision support tool.
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