Harnessing fuzzy logic framework to quantify diagnostic uncertainty in medical decision support

Medical diagnosis has become increasingly difficult, requiring sophisticated systems to manage decision-making uncertainty. The proposed medical diagnosis utilizing fuzzy logic framework (MD-FLF) addresses medical data imprecision and ambiguity by employing fuzzy inference techniques. Clinical data ambiguity is often overlooked by conventional diagnosis models. Such models include probabilistic classifiers and threshold-based decision trees. The models use accurate input-output correlations. However, fuzzy inference simplifies incremental membership assignment and rule-based reasoning in MD-FLF. This enhances the system’s diagnostic ambiguity detection. The framework uses fuzzy rules to represent complicated non-linear interactions between symptoms and diagnostic data. This improves framework interpretation. MD-FLF models’ ambiguity and non-linear relationships between diagnostic inputs and outputs provide interpretable recommendations for complex disorders. Rule-based methods and expert knowledge produce these results. Experimental evaluations showed that MD-FLF improved reliability by 97.68%, uncertainty by 96.84%, ambiguity by 43.56%, patient variability by 98.26%, and diagnostic accuracy by 97.82%. The paradigm addresses uncertainty to increase diagnostic reliability, precision, and confidence while eliminating ambiguity and offering clinical decision-making insights. MD-FLF outperforms deterministic techniques in medical diagnostic decision support systems and is stable and interpretable.