A Semantic Web-Enabled Explainable AI Framework for Interoperable and Scalable Detection of Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a lifelong condition that affects communication, social interaction, and behavior. Artificial intelligence (AI) shows promise for early detection, but many models struggle with accuracy, scalability, and interpretability, limiting clinical use. To address these gaps, this paper proposes a semantic web–enabled explainable AI (XAI) framework for accurate and interoperable ASD diagnosis. The framework has three parts: (1) a semantic data integration layer that harmonizes heterogeneous datasets, (2) a scalable feature engineering process using MapReduce with the Binary Capuchin Search Algorithm (BCSA), and (3) interpretable classifiers enriched with SHAP for transparent predictions. Experiments on ASD datasets achieved about 87% accuracy, outperforming baselines by 7–10% and federated methods by 5%. Precision and F1 improved by 6–8%, while semantic integration enhanced interpretability and trust. By uniting semantic technologies with explainable ML, the framework ensures scalability and offers a reliable, transparent pathway toward clinically useful AI.