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

Abstract

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.

DOI: 10.5281/zenodo.13381954