<![CDATA[Accurate eye disease diagnosis is essential for therapy and vision preservation. Complexity and diversity of symptoms often confound established diagnostic methods. We suggest designing an expert system for eye illness diagnosis utilizing the certainty factor method, which handles ambiguous and imprecise medical diagnoses. A systematic technique to improve diagnostic accuracy is proposed in this research to bridge medical experience and computational reasoning. The research begins with a mathematical framework for symptoms, test data, and diagnostic conclusions. The system uses medical-inspired rule-based inference to aid evidence-based reasoning. The certainty factor technique quantifies diagnosis confidence, ensuring transparency and justifiability. A numerical example shows how to apply the strategy. The simple example shows how the expert system can analyze various criteria and make well-supported diagnoses. It emphasizes symptom analysis, test results, and the certainty factor method's capacity to handle uncertainty. This research emphasizes the interaction between artificial intelligence and medical competence and is conceptual. Real-world application involves medical practitioner participation, intensive testing, and patient data validation. This research advances medical diagnostic tools by combining computational and clinical knowledge. It symbolizes a shift toward efficient, accurate, and transparent diagnostic methods to improve patient care and healthcare.]]>
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