A Multiattribute Decision-Making Framework for Multidisciplinary Lung Cancer Treatment Considering Expert Willingness for Opinion Transformation.

Abstract

Lung cancer, with high morbidity and mortality rates, requires tailored treatment based on pathological subtypes, clinical staging, and individual performance scores. MDT is supposed to improve patient prognosis, making the treatment generation process a multiattribute multiexpert decision-making (MAMEDM) problem. Traditional MAMEDM models often necessitate experts with differing opinions to conform to group consensus, neglecting experts' willingness to adjust opinions. To address this issue, this study proposes an MAMEDM framework which can maximize experts' willingness for opinion transformation. Initially, a linguistic scale function is used to preprocess linguistic evaluations. A fuzzy clustering algorithm is introduced to cluster experts. The weights of subgroups are determined based on the network centrality and the professional titles of experts. Expert opinions within subgroups are aggregated based on the principle of maximizing the willingness of experts for opinion transformation, and then the ORESTE method is implemented to rank treatment options. A case study on lung cancer treatment option generation demonstrates the effectiveness of the proposed framework. Results show that considering experts' willingness to revise evaluations significantly enhances decision acceptance and reduces the impact of noncooperative behaviors.