Developing Knowledge Based System For The Diagnosi Of Tuberculosis Disease And Treatment.

Abstract

Tuberculosis is major public health problem in Ethiopia more than half a century ago. If the disease is not treated well and on time, it can lead to severe health problems like lungs, but can also affect other organs, including the central nervous system. Therefore, detecting those diseases at early stages enable us to overcome and treat them appropriately. 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 prevalence Tuberculosis and the number of death rates imputed to Tuberculosis reached above 29,000 deaths (35 per 100,000) due to TB, excluding HIV related deaths, during the same period, 2014. In an effort to address such problem, this study attempts to design and develop a prototype knowledge-based system that can provide advice for physicians and patients to facilitate the diagnosis and treatment of Tuberculosis patients. To this end, knowledge is acquired using both structured and unstructured interviews from domain experts which are selected using purposive sampling technique from health agents. 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 Tuberculosis 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. The system was evaluated using visual interactive method; it was shown that the system agreed with human expert opinions in 81.25 percent of the decision.