An MCDM approach on Einstein aggregation operators under Bipolar Linear Diophantine Fuzzy Hypersoft Set.

Area of Science:

• Mathematics
• Computer Science
• Medical Science

Background:

• Uncertainty in decision-making poses challenges in complex fields like healthcare.
• Lung carcinoma (lung cancer) is a prevalent and aggressive disease requiring advanced analytical approaches.
• Existing methods may not fully capture the multifaceted nature of cancer data.

Purpose of the Study:

• To implement the Bipolar Linear Diophantine Fuzzy Hypersoft Set and Einstein aggregation operators for handling uncertainty.
• To apply these novel mathematical tools to analyze lung carcinoma data.
• To determine optimal therapeutic strategies for lung cancer based on the developed framework.

Main Methods:

• Development and implementation of the Bipolar Linear Diophantine Fuzzy Hypersoft Set.
• Integration of Einstein aggregation operators within the fuzzy set framework.
• Application of the Bipolar Linear Diophantine Fuzzy Weighted Aggregation Operators to lung carcinoma data, considering various stages and complications.

Main Results:

• Successful implementation of advanced fuzzy set operations for uncertainty management.
• Demonstrated utility of Einstein aggregation operators in decision-making processes.
• Identification of effective therapeutic approaches for lung carcinoma through the proposed methodology.

Conclusions:

• The Bipolar Linear Diophantine Fuzzy Hypersoft Set and associated aggregation operators provide a robust framework for analyzing complex, uncertain data.
• This approach offers innovative solutions for improving lung cancer diagnosis and treatment strategies.
• The study highlights the potential of advanced mathematical models in medical decision-making and therapeutic outcome enhancement.