Fuzzy Logic-Based Medical Diagnosis System for Handling Uncertainty in Healthcare Data
Keywords:
Decision support systems, Fuzzy Inference System (FIS), Fuzzy logic, Healthcare AI, Medical diagnosis, Uncertainty modelingAbstract
Medical diagnosis has always been characterised by its inherent uncertainty due to the lack of precision associated with various symptoms, the differences that exist between patients, and the incompleteness of clinical information available. Traditional decision-making models based on crisp/logical reasoning do not adequately account for such a large amount of uncertainty. In contrast to the use of traditional decision-making models, fuzzy models represent the best method for dealing with vagueness by incorporating linguistic variables (e.g., 'very low' or 'moderate', etc.) and approximate reasoning. This paper focuses on a specific fuzzy logic-based system designed for use within a medical diagnosis application. The system uses fuzzy logic inference mechanisms to map patient inputs (e.g., symptom data, medical history) to the level of severity of the disease (e.g., mild, moderate, severe), and is comprised of the following components: fuzzification; construction of a rule base; model for inference; and defuzzification. The mathematical development, modeling of membership functions, and analysis of this system are presented in detail. The findings from experimentation suggest that the accuracy, interpretability, and robustness of this fuzzy logic-based system are much greater than those of more traditional methods. Therefore, the results of this work point to the efficacy of fuzzy models in supporting real-world healthcare decision-making systems.
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