Unveiling Uncertainty: Neutrosophic Set-Based Algorithms for Robust Decision-Making

Traditional decision-making methods often struggle with inherent uncertainty and imprecision in data. Neutrosophic set-based algorithms offer a novel approach by incorporating degrees of truth, indeterminacy, and falsity. This paper explores the structure and implementation of these algorithms, highlighting their potential to handle real-world complexities. The application of neutrosophic sets in medical diagnosis is presented as a case study, demonstrating how the algorithm aids in evaluating probabilities for various diagnoses under uncertain conditions. The paper concludes by discussing limitations and potential applications in diverse fields.