HybGANN: A Hybrid GAN-GA-ANN Framework for Predicting Diabetes from Imbalanced Medical Data

Abstract The digitization of medical data has enabled large-scale analysis. However, clinical datasets, such as those used for diabetes prediction, often have class imbalances, with disease cases significantly underrepresented. This imbalance poses a major challenge for traditional machine learning models, which tend to favor the majority classes. In addition, many high-performance models operate as black boxes, limiting their adoption in clinical practice due to their lack of interpretability. In this paper, we present HybGANN, a novel hybrid framework that integrates Conditional Tabular Generative Conditional Networks (CTGAN) for synthetic minority data generation, a unique hybrid genetic algorithm (GA) that co-evolves hyperparameters and internal weights from artificial neural networks (ANNs) in a Lamarckian fashion, and SHapley Additive Explanations (SHAP) for post-hoc model interpretability. In contrast to previous work, to the best of our knowledge, this is the first application of a Lamarckian GA for the optimization of node weights and hyperparameters in tabular medical data classification. HybGANN creates a semi-automated workflow that improves predictive performance while providing transparency and adaptability. Applied to a large-scale diabetes dataset, experiments have demonstrated that the HybGANN model outperforms a benchmark ANN network that also uses the same CTGAN pre-balanced dataset on all key classification metrics. The framework achieves a ROC-AUC value of 0.9184 and a PR-AUC value of 0.9268, demonstrating its effectiveness and potential as a reliable AI solution for clinical decision support in imbalanced medical fields.