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A clinical expert system for Noma disease diagnosis
1 Department of Computer Science, Edo State University, Uzairue, Nigeria.
2 Department of Computer Science, University of Benin, Benin City, Nigeria.
Research Article
International Journal of Science and Research Archive, 2024, 13(01), 378–397.
Article DOI: 10.30574/ijsra.2024.13.1.1499
Publication history:
Received on 05 July 2024; revised on 05 September 2024; accepted on 08 September 2024
Abstract:
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.
Keywords:
Expert systems; Noma disease; Medical informatics system; Decision Support Systems; Health Informatics
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
