DESIGN AND IMPLEMENTATION OF A KNOWLEDGE-BASED SYSTEM FOR THE DIAGNOSIS AND TREATMENT OF GASTRITIS DISEASE
ABSTRACT
Gastritis is an inflammation of the stomach lining, which is fairly common and could have different causes. The gastritis diagnosis and treatment skills required by the physicians include clinical experience, good judgment, a sense of humor, stress tolerance, knowledge of available resources, problem-solving ability, and a sense of anticipation. It is therefore important for individuals to have adequate knowledge of different types of gastritis disease to reduce the risk of the disease. Experts systems are a rapidly growing technology and an active area of research in disease diagnosis and treatment. Expert systems use human expert knowledge to solve complex problems in many fields such as Health, science, engineering, business, and weather forecasting. In an effort to address such a problem, this study attempts to design and develop a user-friendly GUI knowledge-based system that can provide advice for physicians and patients to facilitate the diagnosis and treatment of pneumonic patients. To this end, knowledge is acquired using both structured and unstructured interviews from domain experts which are selected using purposive sampling technique from Addis Ababa Bethel Referral Hospital. The relevant documents analysis method is also followed to capture explicit knowledge. Then, the acquired knowledge is modeled using a decision tree that represents concepts and procedures involved in the diagnosis and treatment of pneumonia and the production rule is used to represent the domain knowledge. The knowledge-based system is developed using the integration of two programming languages with the interfacing library of JPL. Knowledge is also extracted from domain experts and document analysis from gastritis treatment. Hence, the prototype of the knowledge-based system which provides gastritis patient diagnosis and treatment was developed using SWI-Prolog 8.2.3 and Java NetBeans IDE 8.2 with JDK 8 for integrating with a graphical user interface respectively. The overall performance achieves 92.8% accuracy with average system performance.
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