Expert system for diagnosing female reproductive disorders using forward chaining - Aulia, Nur Rizky; Rere, Laode Mohammad Rasdi; Kardian, Aqwam Rosadi (Institute of Computer Science (IOCS), 2025)
Early diagnosis increases the chances of successful treatment and prevents the disease from worsening. However, not all women feel comfortable consulting a doctor about their condition. This study aims to develop a web-based expert system capable of diagnosing diseases of the female reproductive system with a user-friendly interface and high accuracy. This application enables women to evaluate the likelihood of potential diseases based on their symptoms and consult a doctor for appropriate treatment. This expert system combines the forward chaining and fuzzy Mamdani methods. Forward chaining identifies possible diseases based on selected symptoms, while fuzzy Mamdani confirms the diagnosis. The disease and symptom data used in this study were gathered through interviews with two obstetricians and gynecologists. The final results of this study show a comparative accuracy level of diagnostic results between the system and experts of 88%.