<![CDATA[Advancements in information technology have brought significant impacts in the healthcare sector, particularly in the medical diagnosis process. Expert systems, as a technological innovation, mimic the capabilities of human experts in making decisions based on knowledge bases and inference rules. The development of expert systems aims to improve the efficiency and accuracy of diagnosis, especially when facing uncertainty and variations in clinical data. This system integrates symptom data, diseases, and prior probabilities derived from epidemiological studies and expert medical experience. In this study, the author designed and implemented an expert system for diagnosing menstrual disorders based on the Bayes’ Theorem method. The system utilizes main components such as a knowledge base, inference engine, and an intuitive user interface. The system workflow begins with the collection of symptom data, calculating probabilities using Bayes’ Theorem, and ultimately delivering probabilistic diagnoses presented informatively to the user. Testing the system demonstrated satisfactory accuracy in identifying menstrual disorders such as menorrhagia, dysmenorrhea, and premenstrual syndrome (PMS). The results show that applying Bayes’ Theorem enhances system reliability in managing data uncertainty and provides diagnosis recommendations based on probability. This system is expected to serve as an effective tool for healthcare professionals and patients for early diagnosis of menstrual disorders while expanding access to accurate and trustworthy health information. Future development will focus on improving the knowledge base and integrating advanced technologies to maximize its benefits in reproductive health.]]>
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