Browsing by Author "Wassie, Getachew"

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    Application of Case-Based Reasoning for Anxiety Disorder Diagnosis
    (Addis Ababa University, 2012-06) Wassie, Getachew; Jemaneh, Getachew (PhD)
    Medical domains have been an application domain of choice for Artificial Intelligence since its founding years in expert systems. The reason for this application is the knowledge complexity presented by the domain, as well as the leading industry market share of healthcare. The success of Artificial Intelligence in different healthcare applications resulted in the emergence of case-based reasoning. The main focus of this research is on application of case-based reasoning in the domain of mental health, specifically the application on a case-based reasoning system for anxiety disorder diagnosis. The main goal of this research is developing a prototype case-based reasoning system that can give decision support for anxiety disorder diagnosticians at a different level of expertise. Overcoming the limitations of a rule-based knowledge base system such as incremental learning and specific knowledge acquisition are the instigation of this research. To achieve the goal of this research the literature has been thoroughly reviewed from both Artificial Intelligence sub-field of case-based reasoning and mental health more specifically anxiety disorder diagnosis literature. For the implementation of the prototype, successfully solved cases are acquired from Amanuel Mental Specialized Hospital. In addition, the main parameters are identified in consultation with anxiety disorder experts. Then, the implementation of the prototype using jCOLIBRI case-based reasoning framework is realized. Finally, testing of the prototype case-based reasoning system is done to evaluate the performance of the system. The testing of the prototype is performed from two sides. The first one is testing in terms of precision and recall and registered 71% and 82% respectively. In addition to this, the average solution similarity using methods Leave One Out evaluator and Hold Out evaluation achieved performance of 73% and 75.5% respectively. The second one is the performance of the system is evaluated by the potential users‟ of the system and achieved 83.2% performance.