Neutrosophic Binary Relation Matrices for Enhanced Medical Diagnosis

Abstract

Medical diagnosis often relies on intricate relationships between symptoms, diseases, and patient data. Traditional methods struggle to account for the inherent uncertainties and indeterminacies within this process. This paper proposes the application of Neutrosophic Binary Relation Matrices (NBRMs) for enhanced uncertainty management in medical diagnosis. NBRMs offer a novel framework by incorporating truth (T), indeterminacy (I), and falsity (F) degrees to represent these relationships. We explore the mathematical structure of NBRMs and discuss their potential in modeling complex symptom-disease associations, incorporating individual patient factors, and developing more nuanced decision support systems. By leveraging NBRMs, we can potentially improve diagnostic accuracy and navigate the complexities of medical data. This paper paves the way for further research on utilizing neutrosophic logic to enhance medical diagnosis and contribute to more informed healthcare decisions.