FCTG: Fuzzy Cognitive Topology Graph for Uncertainty- Aware Medical Diagnosis

Knowledge-graph-augmented large language models (KG-LLMs) are promising for medical diagnosis, but most medical KGs still collapse symptoms and symptom–disease relations into binary links or single-point probabilities, losing the linguistic vagueness and statistical uncertainty inherent in clinical reasoning. We propose the Fuzzy Cognitive Topology Graph (FCTG), a KG-RAG framework that represents both patient-side symptom memberships and graph-side symptom–disease relations as trapezoidal fuzzy numbers, composes them through fuzzy arithmetic, and organizes evidence into confidence nested ($\alpha$-cut) subgraphs. Patient-side fuzzy numbers encode the clarity and severity of symptom descriptions, whereas graph-side fuzzy numbers encode the statistically estimated strength of symptom–disease evidence. FCTG then uses fuzzy-number multiplication and aggregation to rank candidate diseases and assemble structured context for final LLM diagnosis without modifying the LLM itself. On the DDXPlus triage dataset, FCTG achieves 88.0%/98.0% Top-1/Top-3 accuracy, exceeding the strongest scalar KG baseline for each metric by 22.0 and 10.0 percentage points, respectively. On the Gretelai free-text diagnosis dataset, FCTG reaches 74.0% Top-1 accuracy, ahead of the strongest baseline by 4 points. Extensive experiments validate the effectiveness of FCTG in medical diagnosis scenarios.