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

ABSTRACT: 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.

SUBMITTER: Topsir A 

PROVIDER: S-EPMC12315378 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature