Methodology for Building Fuzzy Knowledge Bases to Support Medical Decisions
Keywords:
fuzzy sets, fuzzy knowledge base, fuzzy modelling, fuzzy inference systemAbstract
The article examines key aspects of applying artificial intelligence theory – especially fuzzy logic techniques – to address decision-making challenges in medicine under uncertain conditions. It provides an in-depth methodology for developing fuzzy knowledge bases tailored to the unique characteristics of medical data, taking into account its multifactorial complexity and inherent variability. Emphasis is placed on how fuzzy models enhance processes such as diagnosis, continuous monitoring of patients, and risk evaluation related to anesthetic management before surgery. In particular, the study highlights the necessity of second-order fuzzy logic models, which enable dynamic and flexible data processing while balancing analytical precision with clarity for healthcare professionals. The paper illustrates the use of expert insights formatted as “If-Then” linguistic rules to clarify the relationships among various physiological parameters. Additionally, it outlines procedures for constructing membership functions, implementing second-order fuzzy sets, and applying fuzzy inference algorithms. These approaches demonstrate that integrating fuzzy logic into medical diagnostics not only improves the reliability of preoperative anesthetic risk assessments but also minimizes decision-making errors and optimizes patient treatment protocols.
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