A regret theory-based even swaps method with complex linguistic information and its application in early-stage lung cancer treatment selection

Abstract

Lung cancer is the cancer with the highest morbidity and mortality rate, and selecting treatment options for early-stage lung cancer patients is of great significance in improving cure rate. To help individuals make rational decisions, the even swap method, a multiple criteria decision-making (MCDM) model, provides an effective mechanism to make tradeoffs between criteria; however, the original even swap method ignored psychological characteristics of individuals and could not deal with complex linguistic information. To deal with the early-stage lung cancer treatment selection problem, this paper introduces generalized probabilistic linguistic term sets (GPLTSs) to describe complex linguistic information and proposes a generalized probabilistic linguistic even swap method based on the regret theory. Firstly, the upper and lower bounds corresponding to linguistic expressions are extracted to represent the uncertainty of GPLTSs. Then, a regret-rejoice function is introduced to express psychological characteristics of individuals when they making tradeoffs between criteria. Afterwards, a regret theory-based even swap method is proposed to rank alternatives. The proposed method is validated in the selection of early-stage lung cancer treatment options. Sensitivity analysis and comparative analysis are given to show the feasibility of the proposed method