Enhancing the Explainability of Neuro-Fuzzy Systems with Large Language Models: A Case Study on EEG-Based Epileptic Seizure Classification

This work explores integrating large language models (LLMs) with the adaptive-network-based fuzzy inference system (ANFIS) to enhance interpretability and usability in decision-making processes. ANFIS generates transparent and interpretable fuzzy rules, while LLMs complement these by providing concise, context-aware textual explanations. By combining ANFIS’s data-driven capabilities with the semantic understanding of LLMs, the framework aims to clarify AI outputs and support error identification in the knowledge base. The pipeline implements a human-in-the-loop strategy to engage domain experts in enhancing prompts, verifying explanations, and aligning outputs with expert standards. The methodology was assessed in a medical setting, particularly for predicting epilepsy seizures using EEG data. This study illustrates how the proposed pipeline bridges AI models and real-world applications, providing transparent insights into decision-making processes. It lays the groundwork for creating more interactive, accurate, explainable, and user-friendly tools for predictive analytics, particularly in critical fields like healthcare.