<![CDATA[This research introduces the Diphtheria Diagnosis Expert System (DDES), a pioneering computational tool employing fuzzy logic for the accurate and rapid identification of diphtheria cases. The study builds upon previous research, addressing the limitations of conventional diagnostic methods in recognizing the diverse and evolving presentations of diphtheria. Through meticulous data acquisition from diverse sources, including medical records, public health databases, and historical case studies, a comprehensive dataset was curated, laying the foundation for the DDES's development. The DDES's utilization of fuzzy logic, a dynamic computational framework, empowers it to navigate the intricacies of diphtheria symptomatology. This study demonstrates the system's enhanced diagnostic accuracy, adaptability to varied clinical presentations, and efficiency in delivering rapid outcomes. Comparative analyses with previous research highlight the DDES's advancements, showcasing superior diagnostic precision and speed. Its potential impact on resource optimization within healthcare systems, ethical considerations, and seamless integration into clinical practice are significant contributions to the broader field of infectious disease diagnosis. The expert system's practical utility is emphasized through positive user feedback, indicating its potential acceptance among healthcare professionals. Ethical considerations, including privacy and transparency, are meticulously addressed, aligning the DDES with the highest standards of responsible technological deployment.]]>
