A framework for disease diagnosis based on fuzzy semantic ontology approach. (25th August 2020)

• Record Type: Journal Article
• Authors: Shoaip, Nora; El-Sappagh, Shaker; Barakat, Sherif; Elmogy, Mohammed
• Is Part Of: International journal of medical engineering and informatics. Volume 12:Number 5(2020)
• Page Start: 475
• Page End: 488
• Publication Date: 2020-08-25
• Subjects: diabetes mellitus -- clinical decision support system -- semantic similarity -- fuzzy rule-based systems -- ontology reasoning; 610.2805
• Languages: English
• ISSNs: 1755-0653

Abstract

In this paper, we propose an OWL2 ontology based on SNOMED CT standard medical terminology. It can provide significant help to support physicians in diabetes risk level diagnosis problem. It explicitly defines the semantics of diabetes knowledge by using ontology and deal with the imprecise and vague nature of its data by using fuzzy set theory. It involves building a complete linguistic fuzzy rule-base that can integrate knowledge from expert and CPG with knowledge extracted from training data and knowledge extracted from the semantic model. This step enhances the level of automation and interoperability of CDSS. The importance of our work comes from the current lack of studies related to the integration of the formal integration between the ontology semantics and FES reasoning, especially in the medical domain. The ontology acts as an integral and complementary component of the FES.