A clinical expert system for Noma disease diagnosis

Noma disease, a devastating necrotizing infection primarily affecting children in the poor regions of Africa, poses significant challenges for early diagnosis and intervention. An estimated recurrence rate per 100,000 people in these nations is 20 occurrences per year. Noma disease swiftly spreads to other parts of the mouth, including the lips, tongue, cheek, nose, and teeth. This eventually results in the bones and soft tissues being totally damaged and necrosed over. Children in sub-Saharan Africa, particularly in Burkina Faso, Ethiopia, Mali, Niger, Nigeria, and Senegal, are the most susceptible. This paper, presents an Expert system for diagnosing Noma disease. The expert system analyzes patient symptoms and reliably identifies Noma disease using a rule-based inference engine. Expert knowledge acquired from medical specialists with a focus on Noma disease forms the foundation of the Expert system's knowledge base. Through the user interface implemented as a web application, the Expert system asks users to enter their symptoms. In order to assess the possibility of Noma disease and produce precise diagnoses, it applies the established guidelines and information. The system presented in this work, has various benefits; large volumes of patient data, speed, complexity, and successful analysis of intricate symptom correlations are all capabilities of the proposed system. In addition, it provides recommendations and feedbacks in real-time, assisting medical practitioners in making clinical decisions process.