Abstract
Abstract: We employ fuzzy sets and fuzzy logic for illness diagnosis. By utilizing a fuzzy logic framework, uncertainty in data and decision-making processes may be made up for. Diagnostic models that can manage enormous volumes of complex and raw medical data may be made using fuzzy logic. Fuzzy logic has a number of advantages when it comes to disease detection, having the ability to deal with incomplete and inaccurate data, the ability to incorporate expert knowledge and feedback, the potential to improve diagnostic accuracy and decrease the number of false positives and false negatives, as well as the capacity for handling incomplete and inaccurate data. The method known as "multiple disease detection using fuzzy rules" combines fuzzy logic with rule-based systems to identify various diseases based on symptoms given by the user. The system accepts user-provided symptoms, converts them into fuzzy sets using fuzzy logic. The fuzzy rules are then evaluated using these fuzzy sets, and the degree of integration of each illness is then determined. The level of integration shows the extremely exact likelihood that a user accurately predicts when they will experience a specific circumstance.
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