MAGA: A Controlled Minority-Class Generative-Augmentation Framework With Fairness-Aware Selection for Imbalanced Diabetes Tabular Classification

Imbalanced tabular classification remains a central challenge in clinical risk modeling, where minority-class misses can be costly and subgroup performance disparities are undesirable. This study evaluates augmentation for diabetes-focused classification with six classical oversamplers (SMOTE, ADASYN, BORDERLINE_SMOTE, SVM_SMOTE, KMEANS_SMOTE, SMOTE_BORDERLINE2) and four generative baselines (Copula, TVAE, CTGAN, GREAT). The four generative baselines were executed within the MAGA control workflow, whereas the classical oversamplers were retained as direct end-to-end baselines. We study MAGA (Minority class Augmentation with Generative Approach) as a control framework around minority-focused generative augmentation rather than as a new generator. MAGA links four operational controls: candidate generation, fidelity-based acceptance filtering, adaptive top-up toward target coverage, and fairness-aware model/threshold selection via Composite Validation Utility (CVU), denoted as $J_{\mathrm {VAL}}$ in pipeline outputs. The benchmark covers two datasets (OpenML diabetes and an NHANES-derived diabetes subset), three target imbalance levels (60%, 80%, 100% minority-to-majority), two seeds, and six classifiers. Across 720 matched combinations, GREAT utilizing the MAGA framework attains the highest mean test PR-AUC (0.6374) and mean test recall (0.9063), while SMOTE leads on mean $J_{\mathrm {VAL}}$ (0.6958). At the configuration level, the best observed PR-AUC is obtained by GREAT on OpenML diabetes at the 100% target ratio with HistGB (0.7780), exceeding the best SMOTE result on the same dataset and ratio (0.7592 with LogReg). Holm-corrected pairwise tests show that GREAT significantly outperforms CTGAN, ADASYN, COPULA, BORDERLINE_SMOTE, SVM_SMOTE, and TVAE on PR-AUC, but not the strongest classical baselines. A post-hoc fairness audit using both equal-opportunity disparity and demographic-parity disparity shows that no single method dominates all objectives. Overall, MAGA’s contribution is procedural, providing a reproducible way to connect candidate generation, plausibility control, optional top-up, and fairness-aware operating-point selection in one benchmarked workflow.