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Abstract
Cholera, a highly contagious and potentially deadly disease caused by the Vibrio cholerae bacterium, continues to pose a persistent threat in many regions of the world, particularly in sub-Saharan Africa and South Asia. The disease spreads rapidly, especially in areas with poor water sanitation, and requires prompt detection to prevent widespread outbreaks. Traditional diagnostic methods are often too slow or imprecise to detect early-stage infections, leading to delayed interventions and increased fatalities. To address this challenge, researchers have turned to intelligent diagnostic systems capable of analyzing uncertain, nonlinear, and complex symptom data. Two such systems Fuzzy Expert Systems (FES) and Artificial Neural Networks (ANN) offer powerful complementary tools for early cholera detection. This paper presents a comprehensive review and conceptual discussion of these systems, focusing on their theoretical foundations, implementation processes, real-world applications, and limitations. We also propose a hybrid integration framework that combines the interpretability of fuzzy logic with the learning capabilities of neural networks. This synergy has the potential to revolutionize infectious disease diagnosis by enabling scalable, accurate, and explainable cholera detection systems suitable for deployment in resource-constrained environments.
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