SynthAI: A Generative Symbolic Neuro-Diagnostic Framework for Enhanced Healthcare Analytics

The advent of deep learning has revolutionized numerous fields, including healthcare diagnostics. This paper introduces the Generative Symbolic Neuro-Diagnostic Algorithm (GSNDA), a novel hybrid framework that integrates Generative Adversarial Networks (GANs) with neuro-symbolic AI techniques to enhance diagnostic accuracy and interpretability. GSNDA leverages recurrent neural networks (RNNs) and long short-term memory (LSTM) networks for time-series data. By synthesizing intermediate representations from these diverse sources, the GAN component using DNA technique generates high-fidelity synthetic data, augmenting the training datasets and mitigating class imbalances.The performance of GSNDA is validated on several benchmark healthcare datasets, demonstrating superior accuracy, robustness, and interpretability compared to existing state-of-the-art models. This approach addresses critical challenges in healthcare diagnostics, including data scarcity, class imbalance, and the need for explainable AI in clinical settings