Gann: A Novel Approach for Prediction of Real and Synthetic Indian Liver Patient Data Using GANs and ANN Classification Model

Liver disease, a major global health issue causing approximately 2 million deaths annually, requires accurate predictive models for early detection. This study proposes GANN, a novel framework combining Generative Adversarial Networks (GANs) for synthetic data generation and Artificial Neural Networks (ANNs) for liver disease classification using the Indian Liver Patient Dataset (ILPD). The ILPD, with 583 samples and 10 features, faces challenges like missing values (1.7% in Albumin_and_Globulin_Ratio), class imbalance (416 liver disease vs. 167 non-liver disease cases), and outliers. We address these through preprocessing techniques such as MICE imputation, log transformation, and Proximity Weighted Synthetic Oversampling (PROW). Five GAN variants?CTGAN, Vanilla GAN, Copula GAN, Gaussian Copula GAN, and TVAE?generate 2,000 synthetic samples, validated by Kolmogorov-Smirnov (KS) tests (mean correlation 0.92 with real data). Visualizations, including histograms and correlation matrices, reveal data distributions and relationships. The GANN model achieves 95% accuracy with combined real and synthetic data, compared to 90% with real data alone, outperforming state-of-the-art methods (82?91.2% accuracy). These results suggest GANN?s potential as a robust tool for liver disease prediction, pending further validation.