An Explainable and Probability-Calibrated AdaBoost Framework for Type-2 Diabetes Risk Prediction Using SMOTETomek

Type-2 Diabetes Mellitus (T2DM) is a growing
public health challenge, particularly in low- and middle-income
countries where early risk identification remains critical. This
study proposes an explainable and probability-calibrated Ada
Boost framework for T2DM prediction using a Bangladesh-based
clinical dataset of 1,065 women. Six machine learning classifiers
were evaluated with four imbalance-handling strategies under
stratified 10-fold cross-validation. AdaBoost with SMOTETomek
achieved the best performance and was further optimized using
isotonic probability calibration. The calibrated model achieved
an accuracy of 0.939, recall of 0.999, F1-score of 0.961, and
AUC of 0.905 on an independent test set. Calibration significantly
improved probability reliability, reducing the Brier score from
0.1618 to 0.0620 and Expected Calibration Error from 0.3194 to
0.0542. SHAP analysis identified fasting glucose, systolic blood
pressure, age, and number of pregnancies as the most influential
predictors, while Decision Curve Analysis demonstrated positive
clinical net benefit across relevant threshold ranges. The proposed
framework integrates imbalance correction, calibrated predic
tion, explainability, and clinical utility assessment, providing a
transparent approach for diabetes risk stratification. External
validation on larger multi-center cohorts is required before
clinical deployment.