A knowledge-based decision support system for inferring supportive treatment recommendations for diabetes mellitus

Article type: Research Article

Authors: Ertuğrul, Duygu Çelik^{a; *} | Akcan, Neşe^b | Bitirim, Yiltan^a | Koru, Begum^a | Sevince, Mahmut^a

Affiliations: [a] Department of Computer Engineering, Engineering Faculty, Eastern Mediterranean University, Famagusta, North Cyprus, Turkey | [b] Department of Pediatric Endocrinology, Faculty of Medicine, Near East University, Nicosia, North Cyprus, Turkey

Correspondence: [*] Corresponding author: Duygu Çelik Ertuğrul, Department of Computer Engineering, Engineering Faculty, Eastern Mediterranean University, Famagusta, North Cyprus, Turkey. E-mails: [email protected] and [email protected].

Abstract: BACKGROUND: Diabetes Mellitus (DM) is a significant risk, mostly causing blindness, kidney failure, heart attack, stroke, and lower limb amputation. A Clinical Decision Support System (CDSS) can assist healthcare practitioners in their daily effort and can improve the quality of healthcare provided to DM patients and save time. OBJECTIVE: In this study, a CDSS that can predict DM risk at an early stage has been developed for use by health professionals, general practitioners, hospital clinicians, health educators, and other primary care clinicians. The CDSS infers a set of personalized and suitable supportive treatment suggestions for patients. METHODS: Demographic data (e.g., age, gender, habits), body measurements (e.g., weight, height, waist circumference), comorbid conditions (e.g., autoimmune disease, heart failure), and laboratory data (e.g., IFG, IGT, OGTT, HbA1c) were collected from patients during clinical examinations and used to deduce a DM risk score and a set of personalized and suitable suggestions for the patients with the ontology reasoning ability of the tool. In this study, OWL ontology language, SWRL rule language, Java programming, Protégé ontology editor, SWRL API and OWL API tools, which are well known Semantic Web and ontology engineering tools, are used to develop the ontology reasoning module that provides to deduce a set of appropriate suggestions for a patient evaluated. RESULTS: After our first-round of tests, the consistency of the tool was obtained as 96.5%. At the end of our second-round of tests, the performance was obtained as 100.0% after some necessary rule changes and ontology revisions were done. While the developed semantic medical rules can predict only Type 1 and Type 2 DM in adults, the rules do not yet make DM risk assessments and deduce suggestions for pediatric patients. CONCLUSION: The results obtained are promising in demonstrating the applicability, effectiveness, and efficiency of the tool. It can ensure that necessary precautions are taken in advance by raising awareness of society against the DM risk.

Keywords: Health informatics, diabetes mellitus, knowledge bases, clinical decision support systems, smart healthcare

DOI: 10.3233/THC-230237

Journal: Technology and Health Care, vol. 31, no. 6, pp. 2279-2302, 2023