<![CDATA[Dental diseases, such as caries, periodontitis, and gingivitis, affect public health worldwide, especially in regions where healthcare access remains restricted. The study develops an expert system for early dental disease diagnosis using Forward Chaining and Certainty Factor methods. The system overcomes deficiencies found in previous approaches, such as Naive Bayes and Dempster-Shafer, which demonstrate insufficient accuracy and unclear result interpretation. The developed expert system incorporates a knowledge base containing 7 diseases, 40 symptoms, and 7 diagnostic rules. Forward Chaining enables inference of potential diagnoses from reported symptoms, while the Certainty Factor evaluates diagnostic reliability by calculating confidence levels. System evaluation through Black Box testing achieved 92% diagnostic accuracy, and usability assessments revealed 85% user satisfaction rates, demonstrating that the system proves reliable, accurate, and accessible. Research findings indicate the expert system offers viable solutions for improving dental disease diagnosis in underserved and remote areas, potentially enhancing oral health outcomes through early detection and prompt intervention.]]>
