title: A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis.
abstract: OBJECTIVE
  Case-based reasoning (CBR) is a problem-solving paradigm that uses past knowledge to interpret or solve new problems. It is suitable for experience-based and theory-less problems. Building a semantically intelligent CBR that mimic the expert thinking can solve many problems especially medical ones.
  
  
  METHODS
  Knowledge-intensive CBR using formal ontologies is an evolvement of this paradigm. Ontologies can be used for case representation and storage, and it can be used as a background knowledge. Using standard medical ontologies, such as SNOMED CT, enhances the interoperability and integration with the health care systems. Moreover, utilizing vague or imprecise knowledge further improves the CBR semantic effectiveness. This paper proposes a fuzzy ontology-based CBR framework. It proposes a fuzzy case-base OWL2 ontology, and a fuzzy semantic retrieval algorithm that handles many feature types.
  
  
  METHODS
  This framework is implemented and tested on the diabetes diagnosis problem. The fuzzy ontology is populated with 60 real diabetic cases. The effectiveness of the proposed approach is illustrated with a set of experiments and case studies.
  
  
  RESULTS
  The resulting system can answer complex medical queries related to semantic understanding of medical concepts and handling of vague terms. The resulting fuzzy case-base ontology has 63 concepts, 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, 105 fuzzy datatypes, and 2640 instances. The system achieves an accuracy of 97.67%. We compare our framework with existing CBR systems and a set of five machine-learning classifiers; our system outperforms all of these systems.
  
  
  CONCLUSIONS
  Building an integrated CBR system can improve its performance. Representing CBR knowledge using the fuzzy ontology and building a case retrieval algorithm that treats different features differently improves the accuracy of the resulting systems.
authors: Department of Mathematics, College of Science, King Saud University, PO 2455, Riyadh, Saudi Arabia.
authors: Shaker
authors: S
authors: El-Sappagh
authors: Information Technology Department, Faculty of Computers & Information, Mansoura University, PO 35516, Mansoura, Egypt. Electronic address: melmogy@mans.edu.eg.
authors: Mohammed
authors: M
authors: Elmogy
authors: Information Systems Department, Faculty of Computers & Information, Mansoura University, PO 35516, Mansoura, Egypt.
authors: A M
authors: AM
authors: Riad
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D064229:Biological Ontologies; D003920:Diabetes Mellitus / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D016247:Information Storage and Retrieval; D012660:Semantics; D018875:Vocabulary, Controlled
keywords: Case-based reasoning; Diabetes diagnosis; Fuzzy ontology; Knowledge based system; Semantic retrieval; Standard SNOMED CT terminology