Mild Balanced Neutrosophic Graphs With Application

The framework of mild balanced neutrosophic graphs extends traditional uncertain graph models by incorporating nuanced membership functions that capture truth, indeterminacy, and falsity simultaneously. This multifaceted approach allows for more accurate representation and analysis of uncertain relationships in complex systems. The theoretical foundations laid out through rigorous theorems and propositions provide a robust base for modeling real-world scenarios where ambiguity and partial information are inherent. Moreover, the application to medicinal interactions in diabetic patients demonstrates the potential of these graphs in decision-making processes for optimizing drug combinations, thereby enhancing patient safety and therapeutic outcomes. This study bridges mathematical theory with practical healthcare challenges, highlighting the ver satility and applicability of neutrosophic graph models in interdisciplinary research.