Fuzzy logic has emerged as a powerful mathematical and computational tool for handling uncertainty, imprecision, and vagueness in real-world decision-making systems. Unlike classical binary logic, which operates strictly on true or false values, fuzzy logic allows intermediate degrees of truth and thereby better reflects human reasoning. This research paper examines the application of fuzzy logic in decision-making systems with a special focus on a medical diagnosis case model. The study presents the theoretical background of fuzzy sets, the architecture of a fuzzy inference system, and the design of a rule-based model for estimating disease risk based on patient symptoms and health indicators. The proposed model uses linguistic input variables such as body temperature, blood sugar level, blood pressure, and fatigue level to classify patient health status into low, medium, or high risk. The paper demonstrates that fuzzy logic is highly suitable for medical decision-making because it can process ambiguous and uncertain data in a flexible and interpretable manner. The findings of the study suggest that fuzzy logic-based systems can serve as effective support tools in healthcare diagnostics and can also be extended to various domains such as engineering, education, environmental monitoring, and intelligent control systems.
Fuzzy Logic, Decision-Making Systems, Medical Diagnosis, Fuzzy Inference System, Membership Function, Applied Mathematics, Artificial Intelligence.
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