Title: Intelligent Eye Disease Symptom Checker Using the Clips Expert System Shell
Authors: Hiba waheed Elden Saleh Zorab Samy S. Abu-Naser
Volume: 10
Issue: 7
Pages: 60-75
Publication Date: 2026/07/28
Abstract:
Expert Systems have become an important branch of Artificial Intelligence that supports decision-making and problem-solving in various domains, particularly in healthcare. This research presents the design and development of a rule-based medical advisory system using the CLIPS (C Language Integrated Production System) expert system shell to assist users in identifying potential eye diseases based on their symptoms. The proposed system focuses on common eye disorders and their associated symptoms, providing users with an initial indication of the condition they may be experiencing before visiting a healthcare professional. The system employs a knowledge base consisting of medical rules and expert knowledge represented through a simple and interactive Yes/No questioning approach. Users answer a sequence of symptom-related questions, and the inference engine analyses the responses to determine the most likely eye condition, generate condition-specific recommendations, provide preliminary eye-care guidance, and recommend appropriate medical consultation when necessary. This approach simplifies the diagnostic process, reduces the time required to obtain preliminary health information, and improves access to medical guidance. The proposed expert system is not intended to replace professional medical diagnosis; rather, it serves as an intelligent decision-support tool that helps individuals better understand their symptoms and seek appropriate medical care in a timely manner. Furthermore, the study highlights the role of expert systems in community service by increasing healthcare awareness, reducing unnecessary delays in seeking treatment, and improving the efficiency of medical consultation processes. The findings demonstrate that CLIPS-based expert systems can provide a practical, low-cost, and user-friendly solution for symptom checking and preliminary disease assessment.