Explainable Heterogeneous Graph Neural Networks with Severity-Weighted Edges for Clinical Symptom-Disease Prediction

Automated symptom-checkers built on tabular feature vectors cannot model the relational structure inherent in medical knowledge. We propose an Explainable Heterogeneous Graph Convolutional Network (H-GraphConv) with Severity Weighted Edges for automated clinical diagnosis. Edge weights combine empirical symptom prevalence with validated clinical severity scores, and a Multi-Type Heterogeneous Graph incorporates Disease, Symptom, Anatomical Region, and Lab Test node
types. A Latent Feature Attribution module delivers mathematically exact, pe-rsymptom explanations without approximation, while a Graph-Retrieval Augmented Generation (GraphRAG) pipeline powered by Google Gemini generates clinician-facing diagnostic narratives grounded in GNN-derived graph facts. On a 4,920-record dataset covering 41 diseases and 131 symptoms, the model attains 95.6% link-prediction accuracy, 95.7% F1-score, and AUC-ROC of 0.9412. Under simulated symptom-dropout noise of 0–30%, Top-5 disease-ranking accuracy reaches ≈33%,
which is 2.7× the random baseline. Index Terms—Graph Neural Networks, Explainable AI, Latent Feature Attribution, Link Prediction, Heterogeneous Graphs,
Medical Diagnosis, Symptom Checker, Clinical Decision Support, GraphRAG, PyTorch Geometric.