A RECOMMENDATION SYSTEM FOR ANTI-DIABETIC DRUGS SELECTION BASED ON FUZZY REASONING AND ONTOLOGY TECHNIQUES
Abstract
Diabetes mellitus is a common chronic disease in recent years. According to the World Health Organization, the estimated number of diabetic patients will increase 56% in Asia from the year 2010 to 2025, where the number of anti-diabetic drugs that doctors are able to utilize also increase as the development of pharmaceutical drugs. In this paper, we present a recommendation system for anti-diabetic drugs selection based on fuzzy reasoning and ontology techniques, where fuzzy rules are used to represent knowledge to infer the usability of the classes of anti-diabetic drugs based on fuzzy reasoning techniques. We adopt the "Medical Guidelines for Clinical Practice for the Management of Diabetes Mellitus" provided by the American Association of Clinical Endocrinologists to build the ontology knowledge base. The experimental results show that the proposed anti-diabetic drugs recommendation system gets the same accuracy rate as the one of Chen et al.'s method (R. C. Chen, Y. H. Huang, C. T. Bau and S. M. Chen, Expert Syst. Appl.39(4) (2012) 3995–4006.) and it is better than Chen et al.'s method (R. C. Chen, Y. H. Huang, C. T. Bau and S. M. Chen, Expert Syst. Appl.39(4) (2012) 3995–4006.) due to the fact that it can deal with the semantic degrees of patients' tests and can provide different recommend levels of anti-diabetic drugs. It provides us with a useful way for anti-diabetic drugs selection based on fuzzy reasoning and ontology techniques.
