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

Wajid Ali (), Tanzeela Shaheen, Iftikhar Ul Haq, Hamza Ghazanfar Toor, Tmader Alballa () and Hamiden Abd El-Wahed Khalifa
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Wajid Ali: Department of Mathematics, Air University, PAF Complex E-9, Islamabad 44230, Pakistan
Tanzeela Shaheen: Department of Mathematics, Air University, PAF Complex E-9, Islamabad 44230, Pakistan
Iftikhar Ul Haq: Department of Mathematics, Air University, PAF Complex E-9, Islamabad 44230, Pakistan
Hamza Ghazanfar Toor: Department of Biomedical Engineering, Riphah International University, Islamabad 45320, Pakistan
Tmader Alballa: Department of Mathematics, College of Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia
Hamiden Abd El-Wahed Khalifa: Department of Mathematics, College of Science and Arts, Qassim University, Al-Badaya 51951, Saudi Arabia

Mathematics, 2023, vol. 11, issue 19, 1-18

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.

Keywords: intuitionistic fuzzy sets; three-way decision; decision-theoretic rough sets; power aggregation operators; decision making; optimization; efficiency (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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