Title: An Expert System for Diagnosing Allergy Diseases Using SL5 Object
Authors: Mostafa Naeem Qanoo, Samy S. Abu-Naser
Volume: 10
Issue: 7
Pages: 76-85
Publication Date: 2026/07/28
Abstract:
Allergic diseases represent a significant global health concern, affecting individuals of all ages and causing a wide range of symptoms, from mild discomfort to severe and potentially life-threatening reactions. Early identification and classification of allergy types can improve treatment decisions and reduce unnecessary medical complications. This research presents the design and implementation of an intelligent expert system for diagnosing common allergy diseases using the SL5 Object expert system development environment. The proposed system applies a rule-based knowledge representation approach, where medical knowledge is encoded using symptoms, attributes, and IF-THEN diagnostic rules. The system employs a forward-chaining inference mechanism to analyze user-provided symptoms, including sneezing, itching, watery eyes, skin rash, and breathing difficulties, and generates possible diagnoses such as seasonal allergy, food allergy, drug allergy, and allergic rhinitis. The system was developed by acquiring medical knowledge from domain experts and reliable medical references and transforming this knowledge into a structured knowledge base. The performance of the proposed system was evaluated based on usability, response time, and diagnostic capability through experimental testing with users. The evaluation results demonstrated that the system provides rapid and consistent diagnostic recommendations while maintaining a simple and user-friendly interaction process. The proposed approach highlights the effectiveness of expert systems in supporting preliminary allergy diagnosis and demonstrates the potential of SL5 Object as a suitable tool for developing medical decision-support applications.