Optimized Fuzzy Inference System for Breast Cancer Risk Prediction
DOI:
https://doi.org/10.48047/e190h085Keywords:
Optimized Fuzzy Inference System, Breast Cancer Risk Prediction, Fuzzy Logic, Rule-Based System, Membership Functions, Medical Diagnosis, Decision Support SystemAbstract
Breast cancer remains one of the most prevalent and life-threatening diseases
worldwide, necessitating early and accurate diagnosis for effective treatment. This study
presents an optimized fuzzy inference system (OFIS) for breast cancer risk prediction,
integrating clinical and pathological parameters to enhance diagnostic accuracy. The system
employs triangular membership functions and a rule-based fuzzy logic approach to model the
inherent uncertainty in medical data. An optimized rule base is developed through expert
knowledge and computational tuning, improving the interpretability and reliability of the
predictions. The defuzzification process provides a crisp risk assessment, categorizing patients
into Benign, Suspicious, or Malignant risk levels.
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