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

Key Points

• A novel machine learning framework effectively diagnoses mucopolysaccharidoses with high accuracy, enhancing clinical outcomes.
• Evaluation shows that the explainable AI model achieves 92% diagnostic accuracy over a diverse dataset of cases.
• This analysis uses graph-based neural networks for processing complex relational data, ensuring robust interpretation.
• The findings indicate a need for further research integrating such AI technologies into routine clinical practice.