DiGAN Breakthrough: Advancing diabetic data analysis with innovative GAN-based imbalance correction techniques

In the rapidly evolving field of medical diagnostics, the challenge of imbalanced datasets, particularly in diabetes classification, calls for innovative solutions. The study introduces DiGAN, a groundbreaking approach that leverages the power of Generative Adversarial Networks (GAN) to revolutionize diabetes data analysis. Marking a significant departure from traditional methods, DiGAN applies GANs, typically seen in image processing, to the realm of diabetes data. This novel application is complemented by integrating the unsupervised Laplacian Score for sophisticated feature selection. The pioneering approach not only surpasses the limitations of existing techniques but also sets a new benchmark in classification accuracy with a 90% weighted F1-score, achieving a remarkable improvement of over 20% compared to conventional methods. Additionally, DiGAN demonstrates superior performance over popular SMOTE-based methods in handling extremely imbalanced datasets. This research, focusing on the integrated use of Laplacian Score, GAN, and Random Forest, stands at the forefront of diabetic classification, offering a uniquely effective and innovative solution to the long-standing data imbalance issue in medical diagnostics. •Uniquely applies Generative Adversarial Network, traditionally used in image processing, to diabetes data analysis and classification, achieving a weighted F1 score of 90%, a 20% improvement over traditional methods.•Integrates the unsupervised Laplacian Score for sophisticated feature selection, improving the process of data analysis.•Demonstrates remarkable efficiency in processing extremely imbalanced datasets, outperforming common SMOTE-based techniques.•Combines Laplacian Score, GAN and Random Forest, positioning the study at the forefront of diabetic classification and providing an innovative solution to data imbalance in medical diagnostics.