A Novel Interval-Valued Decision Theoretic Rough Set Model with Intuitionistic Fuzzy Numbers Based on Power Aggregation Operators and Their Application in Medical Diagnosis

Abstract

Intuitionistic fuzzy information is a potent tool for medical diagnosis applications as it can represent imprecise and uncertain data. However, making decisions based on this information can be challenging due to its inherent ambiguity. To overcome this, power aggregation operators can effectively combine various sources of information, including expert opinions and patient data, to arrive at a more accurate diagnosis. The timely and accurate diagnosis of medical conditions is crucial for determining the appropriate treatment plans and improving patient outcomes. In this paper, we developed a novel approach for the three-way decision model by utilizing decision-theoretic rough sets and power aggregation operators. The decision-theoretic rough set approach is essential in medical diagnosis as it can manage vague and uncertain data. The redesign of the model using interval-valued classes for intuitionistic fuzzy information further improved the accuracy of the diagnoses. The intuitionistic fuzzy power weighted average (IFPWA) and intuitionistic fuzzy power weighted geometric (IFPWG) aggregation operators are used to aggregate the attribute values of the information system. The established operators are used to combine information within the intuitionistic fuzzy information system. The outcomes of various alternatives are then transformed into interval-valued classes through discretization. Bayesian decision rules, incorporating expected loss factors, are subsequently generated based on this foundation. This approach helps in effectively combining various sources of information to arrive at more accurate diagnoses. The proposed approach is validated through a medical case study where the participants are classified into three different regions based on their symptoms. In conclusion, the decision-theoretic rough set approach, along with power aggregation operators, can effectively manage vague and uncertain information in medical diagnosis applications. The proposed approach can lead to timely and accurate diagnoses, thereby improving patient outcomes.

On Extended Lr-Norm-Based Derivatives to Intuitionistic Fuzzy Sets

“…Atanassov later conducted additional research to improve intuitionistic fuzzy set theory (IFST) [2,[6][7][8]. In recent years IFST has been increasingly used in applications of decision-making problems [9,10], medical diagnosis [11][12][13], software selection [14], environmental management [15], transport problems [16], predator prey [17], etc. Susanto et al [18] generated fuzzy interval data from crisp data using the Cheng et al [19] correlation method to determine the relationship between students' anxiety and mathematical self-efficacy, based on the concept of α-cut from a fuzzy set.…”

An Improved Intuitionistic Fuzzy Decision-Theoretic Rough Set Model and Its Application

“…Liang et al [44,45] developed the notion of intuitionistic decision-theoretic rough sets (DTRS) by fusing the idea of three-way decision-theoretic rough sets with intuitionistic fuzzy sets and demonstrated its application with examples. Researchers have explored the 3WD approach of DTRS with various applications [12,13,[46][47][48][49].…”