Thyroid disease classification using generative adversarial networks and Kolmogorov-Arnold network for three-class classification.

Thyroid disease classification is a critical challenge in medical diagnostics, requiring accurate differentiation between hyperthyroidism, hypothyroidism, and normal thyroid function. This study introduces an advanced machine learning approach that integrates generative adversarial networks (GANs) for data augmentation and Kolmogorov-Arnold networks (KANs) for classification. Various machine learning models including logistic regression, random forest, support vector machines, multilayer perceptrons, and KANs were trained and evaluated. The results indicate that the application of GAN-based data augmentation has significantly improved classification accuracy, particularly for minority classes. Specifically, the KAN model achieved an accuracy of 98.68% and random forest (RF) F1-score of 98.00%, outperforming traditional neural network applications. The results demonstrate that GAN-augmented datasets significantly improve classification accuracy, and the KAN model achieves superior performance and generalization capabilities compared to traditional neural networks. Additionally, the SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) were employed to ensure model transparency and interpretability. These explainability methods highlight thyroid stimulating hormone as the most prominent feature in classification, further supporting its clinical utility in the diagnosis of thyroid diseases. The findings underscore the potential of advanced AI-driven techniques in improving thyroid disease classification, addressing class imbalance, and enhancing explainability in healthcare applications. By leveraging synthetic data generation, this study provides a feasible framework for actual clinical application, particularly in situations where clinical data are limited or imbalanced. The integration of GANs and KANs enhances diagnostic accuracy while preserving robustness and generalizability to diverse patient populations. Besides, the approach fosters the deployment of explainable AI models in clinical decision support systems so that healthcare practitioners can make improved and more reliable decisions, thus leading to better patient outcomes and resource allocation.