A Self-learning Knowledge Based System for Diagnosis and Treatment of Diabetes
| dc.contributor.advisor | Meshesha, Million(PhD) | |
| dc.contributor.author | Geberemariam, Solomon | |
| dc.date.accessioned | 2018-11-28T11:44:30Z | |
| dc.date.accessioned | 2023-11-18T12:44:04Z | |
| dc.date.available | 2018-11-28T11:44:30Z | |
| dc.date.available | 2023-11-18T12:44:04Z | |
| dc.date.issued | 2013-01 | |
| dc.description.abstract | 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 study attempts to design and develop a prototype self-learning knowledge-based system that can provide advice for physicians and patients to facilitate the diagnosis and treatment of diabetic patients. To this end, knowledge is acquired using both structured and unstructured interviews from domain experts which are selected using purposive sampling technique from Black Lion Hospital Diabetes Center. Relevant documents analysis method is also followed to capture explicit knowledge. Then, the acquired knowledge is modeled using decision tree that represents concepts and procedures involved in diagnosis and treatment of diabetes and production rules are used to represent the domain knowledge and knowledge-based system is developed using SWI Prolog editor tool. It uses backward chaining which begins with possible solutions or goals and tries to gather information that verifies the solution. Moreover, in testing and evaluating the prototype system eighteen patients‟ history are selected in order to test the accuracy of the prototype system and also for ensuring whether the prototype system satisfies the requirements of its end-users or not. Thus, the overall total performance of the prototype system is 84.2%. The prototype system achieves a good performance and meets the objectives of the study. However, in order to make the system applicable in the domain area for diagnosis and treatment of diabetes additional study is needed like updating the rules in the knowledge base of the system automatically, incorporating a well designed user interface and a mechanism of NLP facilities. Keywords: Knowledge-Based System, Self-learning, Diabetes. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/14639 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Knowledge-Based System, | en_US |
| dc.subject | Self-learning, | en_US |
| dc.subject | Diabetes | en_US |
| dc.title | A Self-learning Knowledge Based System for Diagnosis and Treatment of Diabetes | en_US |
| dc.type | Thesis | en_US |