School of Information Science

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Browsing School of Information Science by Subject "(CBR) in solving"

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    A Case-Based Approach for Designing Knowledge-Based System for AIDS Resource Center (ARC): The Case of Warmline Clinician Consultation Service
    (Addis Ababa University, 2010-06) Jorgi, Alemu; Jemaneh, Getachew (PhD)
    The study was conducted with the aim to explore the potential of case-based reasoning (CBR) in solving complex side effects of HIV/AIDS cases for PLWHA who have already begun antiretroviral therapy. The knowledge was acquired from domain experts using semi-structured interview and discussion. Then knowledge modeling was employed using hierarchical tree structure that represents concepts and parameters very easily. Based on the model, the knowledge was represented using the case-based reasoning approach. The CBR system was developed using JCOLIBRI version 1.1, which is the most compatible and reliable CBR tool to deal with case-based system. The most important part of a Case-Based Reasoning model includes case retrieval; the similarity measuring stage, reuse; which allows domain expert to transfer retrieval case solution to suit for the current case, revise; to test the solution and retain to store the confirmed solution to the casebase. For a new case patient, whose recommendations was not yet to be confirmed and who had an indefinite circumstances, the CBR model was effectively used to retrieve solution for the new case at hand based on previous similar solved cases . These similar cases could provide useful information to the clinicians, in reaching a potential solution for the new case. The prototype developed in this study was evaluated to measure its performance. User acceptance test and statistical analysis (i.e. precision and recall) were the two scenarios that the researcher used to evaluate the performance of the prototype for this study. As evaluated by domain experts in the Warmline, the performance of prototype showed promising result. Moreover, the performance of the prototype was evaluated using recall/precision calculations. Thus, an average recall value of 72%, with an average precision of 63% has been achieved in the study. Encouraging results were registered for overall performance of the prototype. In general, the system has basically achieved the goal since the main objective of the system was to find the potential useful similar cases efficiently for the users.