Explainable AI-driven graph-based neural networks for mucopolysaccharidoses diagnosis

BACKGROUND: Mucopolysaccharidoses (MPS) are a group of rare lysosomal storage disorders caused by enzyme deficiencies leading to the accumulation of glycosaminoglycans (GAGs). Their clinical heterogeneity and low prevalence contribute to delayed and often missed diagnoses. Early detection is critical for improving treatment outcomes. This study investigates the utility of Graph Neural Networks (GNNs) for diagnosing MPS using real-world electronic health records (EHRs). METHODS: Diagnostic features were extracted for 106 subjects (37 MPS and 69 controls) from the SEHA health system in Abu Dhabi. Four GNN architectures were trained and evaluated across seven feature selection strategies using nested stratified cross-validation. Model interpretability was assessed using Shapley Additive exPlanations (SHAP) to rank diagnostic features and PGExplainer to provide patient-level graph-topology explanations. RESULTS: The Graph Convolutional Network (GCN) combined with chi-square feature selection achieved the highest performance, with an AUC of 0.97 [95% CI: 0.93–1.00], sensitivity/specificity of 0.97/0.94, PPV/NPV of 0.9/0.98, F1-score of 0.93, and accuracy of 0.95. SHAP analysis highlighted clinically coherent diagnostic features aligned with the domain expert-driven features, while PGExplainer identified compact relational subgraphs that characterized MPS cases, complementing the global feature-level interpretation. CONCLUSION: The proposed graph-based diagnostic framework demonstrates strong potential for early MPS diagnosis using EHR data typically available in the clinical setting. By integrating high-performing predictive modeling with both global and patient-level interpretability, this approach provides a promising foundation for developing clinically meaningful and data-driven screening tools for rare diseases, particularly in the context of limited data availability.