An Expert System for Early Risk Diagnosis of Breast Cancer Using Fuzzy Mamdani and Case-Based Reasoning

Breast cancer remains one of the leading causes of morbidity and mortality among women worldwide, making early detection essential to improve treatment outcomes. However, early-stage breast cancer symptoms are often subjective and non-specific, which complicates initial risk assessment. This study proposes an expert system for early breast cancer risk diagnosis by integrating Fuzzy Mamdani and Case-Based Reasoning (CBR). The Fuzzy Mamdani method is employed as the primary inference mechanism to model uncertainty in symptoms and risk factors using linguistic rules, while CBR is utilized as a decision support component by leveraging similarities with previously validated clinical cases. The dataset consists of 150 patient records, of which 123 cases are used as the case base and 27 cases are employed for system evaluation. Experimental results show that the proposed system achieves an accuracy of 92.59% compared to expert judgments. These findings indicate that the integration of Fuzzy Mamdani and Case-Based Reasoning provides an interpretable and adaptive approach for early breast cancer risk assessment and has potential as a screening support tool.