Breast cancer remains a critical global health concern, necessitating
advanced and accurate diagnostic tools. This study introduces an
Ensemble Neuro-Fuzzy Algorithm (ENFA) designed for the detection
and classification of breast cancer. In the background, we address the
limitations of existing methods, emphasizing the need for enhanced
accuracy and interpretability in diagnostic models. The methodology
involves the fusion of neuro-fuzzy systems within an ensemble
framework, leveraging the complementary strengths of both neural
networks and fuzzy logic. The primary contribution lies in the
development of a robust ENFA, which not only improves diagnostic
accuracy but also provides interpretable insights into decision-making
processes. The ensemble nature of the algorithm enhances resilience
and generalization across diverse patient profiles. Experimental results
demonstrate superior performance compared to existing methods,
showcasing heightened sensitivity and specificity in breast cancer
detection. The findings underscore the potential of ENFA as a reliable
tool for early and accurate breast cancer diagnosis. This research
signifies a significant step towards advancing the efficacy of
computational models in medical diagnostics.