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

Author

Listed:
  • 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
    Department of Operations and Management Research, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt)

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.

Suggested Citation

  • Wajid Ali & Tanzeela Shaheen & Iftikhar Ul Haq & Hamza Ghazanfar Toor & Tmader Alballa & Hamiden Abd El-Wahed Khalifa, 2023. "A Novel Interval-Valued Decision Theoretic Rough Set Model with Intuitionistic Fuzzy Numbers Based on Power Aggregation Operators and Their Application in Medical Diagnosis," Mathematics, MDPI, vol. 11(19), pages 1-18, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4153-:d:1252808
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    References listed on IDEAS

    as
    1. Junhua Hu & Dan Chen & Pei Liang, 2019. "A Novel Interval Three-Way Concept Lattice Model with Its Application in Medical Diagnosis," Mathematics, MDPI, vol. 7(1), pages 1-14, January.
    2. Wajid Ali & Tanzeela Shaheen & Hamza Ghazanfar Toor & Faraz Akram & Md. Zia Uddin & Mohammad Mehedi Hassan, 2023. "An Innovative Decision Model Utilizing Intuitionistic Hesitant Fuzzy Aczel-Alsina Aggregation Operators and Its Application," Mathematics, MDPI, vol. 11(12), pages 1-22, June.
    3. Pawan Gora & V.P. Tomar, 2023. "Intuitionistic Fuzzy Modulus Similarity Measure," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 15(1), pages 1-22, January.
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    Cited by:

    1. A. S. Wungreiphi & Fokrul Alom Mazarbhuiya & Mohamed Shenify, 2023. "On Extended L r -Norm-Based Derivatives to Intuitionistic Fuzzy Sets," Mathematics, MDPI, vol. 12(1), pages 1-19, December.

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