Publications by authors named "Anietie John"

Article Synopsis
  • Malaria and Typhoid fever are major health issues in tropical areas, worsened by unclear treatment protocols, drug resistance, and environmental conditions, making quick and correct diagnoses essential to reduce death rates.
  • Traditional diagnostic methods struggle due to overlapping symptoms; however, using machine learning models and explainable AI (XAI) techniques can provide better insights into these diseases by clarifying how decisions are made.
  • The study shows that the Random Forest model, along with tools like LIME and GPT, can enhance diagnostic transparency, but the overall effectiveness is limited by dataset quality and challenges related to real-time application and internet dependency.
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The report of the World Health Organization (WHO) about the poor accessibility of people living in low-to-middle-income countries to medical facilities and personnel has been a concern to both professionals and nonprofessionals in healthcare. This poor accessibility has led to high morbidity and mortality rates in tropical regions, especially when such a disease presents itself with confusable symptoms that are not easily differentiable by inexperienced doctors, such as those found in febrile diseases. This prompted the development of the fuzzy cognitive map (FCM) model to serve as a decision-support tool for medical health workers in the diagnosis of febrile diseases.

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