Fuzzy logic and linear regression modelling in breast cancer detection: A review - Ahmed, Reham A.; Shafi, Muhammad Ammar; Rashid, Nor Faezan Abdul; Othman, Suraya; Badeel, Rozin; Abdal, Banan Badeel (Edelweiss Applied Science and Technology, 2025)
The research investigates the effectiveness of breast cancer detection using linear regression models and fuzzy logic approaches, together with an analysis of their medical diagnostic applications and their associated limitations. The research evaluates performance results by analyzing both methods through a review of current studies, where linear regression demonstrates ease of interpretation alongside simplicity, but fuzzy logic shows strength in dealing with uncertainty along with nonlinear relationships. The research shows that while linear regression works simply, it fails to handle the complexity of medical data, but fuzzy logic handles complex medical diagnosis settings better, which suggests that adding fuzzy logic features to linear regression can boost diagnosis quality. The research finds that the hybrid technique involving fuzzy logic and linear regression may increase the accuracy of breast cancer detection. Furthermore, it highlights the requirement for further investigation of sophisticated artificial intelligence strategies, like neural networks, for addressing the basic techniques’ limitations. Practical Implications: The study offers a useful guide to medical and research professionals, indicating that beyond the intriguing integration of fuzzy logic from AI capabilities, there is the potential to improve diagnostic performance in a clinical setting. Future developments in computer AI-driven models will certainly create an even better workflow for breast cancer examination.