AUTHOR=Rasheed Muhammad Waheed , Mahboob Abid , Mustafa Anfal Nabeel , Badi Israa , Ali Zainab Abdulkhaleq Ahmed , Feza Zainb H. 

TITLE=Enhancing breast cancer treatment selection through 2TLIVq-ROFS-based multi-attribute group decision making

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=7

YEAR=2024

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1402719

DOI=10.3389/frai.2024.1402719

ISSN=2624-8212

ABSTRACT=<sec><title>Introduction</title><p>Breast cancer is an extremely common and potentially fatal illness that impacts millions of women worldwide. Multiple criteria and inclinations must be taken into account when selecting the optimal treatment option for each patient.</p></sec><sec><title>Methods</title><p>The selection of breast cancer treatments can be modeled as a multi-attribute group decision-making (MAGDM) problem, in which a group of experts evaluate and rank alternative treatments based on multiple attributes. MAGDM methods can aid in enhancing the quality and efficacy of breast cancer treatment selection decisions. For this purpose, we introduce the concept of a 2-tuple linguistic interval-valued <italic>q</italic>-rung orthopair fuzzy set (2TLIV<italic>q</italic>-ROFS), a new development in fuzzy set theory that incorporates the characteristics of interval-valued <italic>q</italic>-rung orthopair fuzzy set (IV<italic>q</italic>-ROFS) and 2-tuple linguistic terms. It can express the quantitative and qualitative aspects of uncertain information, as well as the decision-makers' level of satisfaction and dissatisfaction.</p></sec><sec><title>Results</title><p>Then, the 2TLIV<italic>q</italic>-ROF weighted average (2TLIV<italic>q</italic>-ROFWA) operator and the 2TLIV<italic>q</italic>-ROF weighted geometric (2TLIV<italic>q</italic>-ROFWJ) operator are introduced as two new aggregation operators. In addition, the multi-attribute border approximation area comparison (MABAC) method is extended to solve the MAGDM problem with 2TLIV<italic>q</italic>-ROF information.</p></sec><sec><title>Discussion</title><p>To demonstrate the efficacy and applicability of the suggested model, a case study of selecting the optimal breast cancer treatment is presented. The results of the computations show that the suggested MAGDM model is able to handle imprecision and subjectivity in complicated decision-making scenarios and opens new research scenarios for scholars.</p></sec>