Integrating Semantic Knowledge Representation and Machine Learning for Intelligent Disease Surveillance

Disease surveillance plays a fundamental role in public health by enabling the timely detection, monitoring, and management of infectious disease outbreaks. However, existing surveillance platforms often face challenges related to fragmented healthcare data, semantic heterogeneity, delayed reporting, limited interoperability, and insufficient predictive capabilities. These limitations significantly hinder rapid outbreak response and evidence-based decision-making, particularly in resource-constrained healthcare environments. This study proposes an intelligent disease surveillance framework that integrates Semantic Knowledge Representation (SKR), Healthcare Knowledge Graphs (HKGs), and Machine Learning (ML) to enhance disease monitoring, prediction, and explainability. The framework employs ontology-based semantic modeling using RDF and OWL standards to harmonize heterogeneous healthcare data originating from electronic health records, laboratory information systems, public health databases, environmental monitoring platforms, and mobile health applications. A healthcare knowledge graph is constructed to represent semantic relationships among patients, diseases, symptoms, pathogens, environmental conditions, healthcare facilities, and geographical locations. Semantic reasoning mechanisms are applied to enrich the data prior to machine learning analysis. The enriched dataset is subsequently utilized by supervised learning algorithms for disease classification, outbreak prediction, and epidemiological trend analysis. Experimental evaluation conducted on integrated healthcare datasets demonstrates that the proposed SKR-ML framework achieves superior performance compared with conventional machine learning approaches, attaining an accuracy of 97.8%, precision of 97.1%, recall of 96.8%, and F1-score of 96.9%. Furthermore, semantic enrichment enhances model interpretability by providing ontology-driven explanations for prediction outcomes. The proposed framework offers a scalable, interoperable, explainable, and intelligent solution for next-generation disease surveillance and public health decision support systems.