<![CDATA[The urgency of this research arises from the high prevalence of dental health problems and the limited public access to early diagnostic services, which often leads to delayed treatment and more severe oral diseases. This study aims to develop and evaluate an expert system application capable of supporting preliminary dental health detection using a forward-chaining inference approach. The research adopts a systematic development methodology encompassing problem identification, knowledge acquisition, rule formulation, system design, implementation, and evaluation. Diagnostic knowledge was obtained from dental literature and expert consultations, yielding a dataset comprising 20 dental diseases and 58 associated symptoms. These data were structured into a rule-based knowledge base and processed through a forward-chaining inference engine. The system was implemented as a web-based application using PHP and MySQL to ensure accessibility and scalability. The results demonstrate that the proposed system accurately infers dental conditions from user-input symptoms, achieving diagnostic accuracy of up to 94% in complex test cases and 100% in controlled validation scenarios. This research contributes to the field of health informatics by demonstrating the effectiveness of Forward Chaining-based expert systems for preliminary dental diagnosis and by providing an accessible decision-support tool that can enhance public awareness, promote early detection, and support timely professional consultation.]]>
