Multi-Crititerion and Multi-Objective Group Decision System for Breast Carcinoma TOPSIS Framework in Hesitant Fuzzy

Abstract

The field of breast cancer research has seen an increase in the development of predictive models in the previous few years. The study's goal is to make predictions about the types of breast cancer patients who will be diagnosed and how they will be outranked based on the severity of their condition. Despite the abundance of techniques available, only a select few were used in the study, resulting in a TOPSIS that was reluctant and ambiguous. The goal of the study is to discover the correlation coefficient between the tuples and to determine which patients may be efficiently treated with the most recent drugs. All the components of TOPSIS, such as the ideal positive solution and the ideal negative solution, contribute to a high degree of accuracy in rating disorders. Hesitant TOPSIS technique found challenges in order preference by resemblance to an ideal answer. The post-processing work here is distinct from the other pre-processing work that is mentioned.