Hyperacusis

Hyperacusis, characterized by increased sensitivity to sounds, presents growing challenges for effective diagnosis and intervention. This study utilizes graph theoretical metrics, specifically the calculation of the metric dimension of a graph to visualize and quantify the intricate network of interactions within the etiological landscape of Hyperacusis and it‟s potential causes. By constructing a graph H = (V,E) , where nodes (V) represent factors such as aging, trauma, infections, depression, and migraines, and edges (E) model their interactions, we calculate the metric dimension (β (H)) to identify critical factors. To further address the inherent complexity of calculating the resolving set for such a complex graph, the shortest path algorithm was utilized to determine the minimal resolving set, enhancing the precision and efficiency of the metric dimension analysis. This network-centric approach provides insights into the importance and interdependencies of these causes, reducing the complexity from thirteen possible factors to six key contributors. These findings offer a framework for targeted clinical interventions, contributing to the broader understanding of Hyperacusis etiology and improving patient management.