Diabetes Case Reports

Abstract

A Decision Support System for Diabetes Mellitus Management

Shaker El-Sappagh and Mohammed Elmogy

Diabetes mellitus is considered as a dangerous chronic disease. Diagnosis is the first step in its management. Clinical decision support system (CDSS) for diabetes diagnosis improves its detection and decreases the opportunity for its complications. However, its diagnosis is a theory-less problem. Case-based reasoning (CBR) is a problem-solving paradigm that uses past experiences to solve new problems. Integration of CBR and formal ontologies enhances the intelligence of this paradigm. Utilizing patients’ electronic health records (EHRs) for building case-base knowledge solves the problem of knowledge acquisition bottleneck; however, preparation steps are required. Moreover, using standard medical ontologies, such as SNOMED-CT, enhances the interoperability and integration of CDSS with the healthcare system. If ontology-based CBR systems utilize vague or imprecise knowledge, the semantic effectiveness is further improved. This paper proposes an advanced and complete fuzzy-ontology-based CBR framework that manages and utilizes imprecise knowledge. We implement the most critical steps in CBR (i.e., case representation and retrieval). The implemented framework has been tested on the diabetes diagnosis problem using a case-base of 60 real cases from The EHR of the Mansoura University Hospitals, Mansoura, Egypt. The proposed system has an accuracy of 97.67%.