Developing an Ontology-Driven Automated Healthcare Framework with SWRL for Disease Identification

This paper introduces a novel AI-driven ontologybased framework for disease diagnosis and prediction, leveraging the advancements in machine learning and data mining. We have constructed a comprehensive ontology that maps the complex relationships between a multitude of diseases and their manifested symptoms. Utilizing Semantic Web Rule Language (SWRL), we have engineered a set of robust rules that facilitate the intelligent prediction of diseases, embodying the principles of NLP for enhanced interpretability. The developed system operates in two fundamental stages. Initially, we define a sophisticated class hierarchy within our ontology, detailing the intricate object and data properties with precision—a process that showcases our application of computer vision techniques to interpret and categorize medical imagery. The second stage focuses on the application of AI-powered rules, which are executed to systematically extract and present detailed disease information, including symptomatology, adhering to established medical protocols. The efficacy of our ontology is validated through extensive evaluations, demonstrating its capability to not only accurately diagnose but also predict diseases, with a particular emphasis on the AI methodologies employed. Furthermore, the system calculates a final risk score for the user, derived from a meticulous analysis of the results. This score is a testament to the seamless integration of AI and ML in developing a user-centric diagnostic tool, promising a significant impact on future research in AI, ML, NLP, and robotics within the medical domain.