<![CDATA[In this modern era, eating disorders in children are receiving increasing attention due to their significant impact on physical health, mental well-being, and overall development. Diagnosing eating disorders in children is often complicated due to the varying symptoms and the difficulty children have in articulating or understanding their conditions. Additionally, traditional diagnostic approaches often rely on subjective assessments and clinical expertise, which can lead to misdiagnosis or delays in appropriate intervention. This study aims to develop an expert system for diagnosing eating disorders in children using Dempster-Shafer Theory as the primary inference engine. This approach is chosen for its strengths in handling uncertainty and incomplete information, allowing the system to make diagnostic inferences based on observed symptoms, even when the information is ambiguous or incomplete. The system is designed to leverage computational algorithms and medical knowledge, providing reliable and consistent results. From tests conducted on a sample of 30 randomly selected cases, the system achieved an accuracy rate of 93.33%, demonstrating that this approach is highly effective in diagnosing eating disorders in children. The developed expert system, web-based, is equipped with key features that enable diagnosis based on symptoms and provide diagnostic results along with recommendations for appropriate medical actions.]]>
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