A Hybrid Decision Making Framework with Pythagorean Fuzzy Information for Childhood Cancer Prognosis

Medical information is complex and confidential, making accurate diagnosis challenging, especially with varying expert opinions. This study proposes a hybrid multicriteria decision-making (MCDM) approach combining the Pythagorean fuzzy method based on removal effects of criteria (PF-MEREC) and stepwise weight assessment ratio analysis (SWARA). By integrating both objective and subjective weight assessments, the approach ranks alternatives using TOPSIS. This method offers a novel solution to early childhood cancer diagnosis, demonstrating its practicality and efficiency through sensitivity analysis. The results align with the standards of the American Cancer Society and the National Cancer Institute, showcasing its potential to improve diagnosis and treatment planning.