Semantic Web Based Healthcare Recommendation System: the Case of Diabetics

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Addis Ababa University


This work proposes a Recommendation System in diabetes healthcare in order to improve the quality of life of Diabetes patients. Diabetes is a permanent disease in which the human body’s cells either do not respond properly to insulin, or insulin production is insufficient. If the disease is not treated well and on time, it can lead to severe health problems like heart disease, blindness, failure of kidney, and amputations of the lower extremity. Therefore, this chronic disease needs dietary control, physical exercise and insulin management. However, among people in the developing countries like Ethiopia, permanent diseases are growing to be causes of death. These problems are becoming worse due to the scarcity of specialists, practitioners and health facilities. In Ethiopia, there has been observed a threat of increased diabetes prevalence and the number of death rates imputed to diabetes reached above 21,000 in 2007. In an effort to address such problem, this project attempts to design and develop a prototype semantic web based healthcare recommendation system that can provide advice for physicians and patients to facilitate the diagnosis and treatment of diabetic patients. To this end, we use semantic technology for building ontology knowledge repository to provide data integration and medical recommendations for diabetes management. First, we build the ontology of diabetic knowledge is acquired using interviews from domain experts which are from Black Lion Hospital Diabetes Center. Relevant documents analysis method is also followed to capture knowledge. Then, the acquired knowledge is modeled using ontology that represents concepts involved in diagnosis and treatment of diabetes and to enter and link concepts and data for diabetes ontology, we used Protégé-owl editor tool. The ontology model provides knowledge into which information on individual patient including blood glucose examination information and recommendation are derived. Based on ontology’s structure, the model can collect, store and share information from heterogeneous sources, Reason over knowledge. Furthermore, ontology has been proven a better way of describing managed data. In this project, we mainly focus on the ontology development process for Type II diabetes only.



Ontology, Owl, Swrl, Healthcare, Recommendation System