A FUZZY LOGIC FRAMEWORK FOR BREAST CANCER TREATMENT OPTIMIZATION BASED ON RACE, AGE, AND TUMOR SIZE

In modern oncology, data complexity can challenge physicians in selecting optimal treatment paths. This study introduces a fuzzy logic-based decision support system that evaluates three variables—race, age, and tumor size—to recommend preferred treatment options for breast cancer. Leveraging fuzzy logic enables nuanced clinical assessments beyond binary reasoning, thus reducing decision-making time and minimizing potential errors. Results highlight fuzzy logic's capability to mirror human decision-making in oncology and offer a scalable method for personalized treatment optimization.