Fuzzy Logic Applications in Medical Diagnosis

Fuzzy logic provides a computational framework for reasoning under uncertainty, reflecting the imprecision inherent in many clinical parameters. In medical diagnosis, physiological measurements, symptom descriptions and laboratory data often defy crisp categorisation. Fuzzy logic formalises these ambiguities through graded membership functions and rule-based inference, enabling decision-support systems to interpret borderline cases and integrate expert knowledge in a transparent manner. Applications span cardiovascular, neurological and musculoskeletal disorders, where fuzzy rule-based classifiers and inference engines have enhanced the sensitivity and specificity of diagnostic algorithms. By accommodating linguistic variables such as “high risk”, “moderate pain” or “mild abnormality”, these systems bridge quantitative data and clinical reasoning, offering intuitive outputs for practitioners. Advances in hybrid models that couple fuzzy logic with neural networks or probabilistic methods have further refined diagnostic accuracy and adaptive learning. Globally, fuzzy logic tools have been deployed in both resource-rich and low-resource settings, underscoring their flexibility and cost-effectiveness. Ongoing research seeks to embed these approaches within electronic health records and telemedicine platforms, driving real-time decision support and personalised care pathways.

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Fuzzy Logic Applications in Medical Diagnosis publication trend

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Technical terms

Fuzzy set: A collection in which elements have degrees of membership ranging from 0 to 1, modelling vague concepts.

Membership function: A mathematical function that assigns to each input value a membership grade in a fuzzy set.

Fuzzy inference system: A decision-making framework that applies fuzzy rules to translate inputs into outputs through fuzzification and defuzzification.

Linguistic variable: A variable characterised by qualitative terms (e.g., “high”, “low”) rather than precise numerical values.

Rule base: A set of if–then statements that encode expert knowledge for fuzzy reasoning.

References

1. Fuzzy Logic in Medicine and Bioinformatics. BioMed Research International (2006).
2. Fuzzy Rule‐Based Classification System for Assessing Coronary Artery Disease. Computational and Mathematical Methods in Medicine (2015).
3. A Novel Fuzzy Expert System for the Identification of Severity of Carpal Tunnel Syndrome. BioMed Research International (2013).
4. The Use of Fuzzy BackPropagation Neural Networks for the Early Diagnosis of Hypoxic Ischemic Encephalopathy in Newborns. BioMed Research International (2011).