APPLICATION OF AUM BLOCK GEOMETRIC MEAN LABELING TO POLYCYSTIC OVARY SYNDROME ANALYSIS

Abstract

Polycystic Ovary Syndrome (PCOS) is a hormonal imbalance, commonly observed in women and is associated with a physical and metabolic symptoms. In this study, AUM Block Geometric Mean labeling is applied to examine the severity of symptoms, analyse the effectiveness of the treatment, and identify the most suitable treatment type for managing PCOS. To support this mathematical model furthermore, the Chi-square method is used to evaluate the relationship between the treatment categories and the responses collected through a structured public survey. A cycle cactus graph is used to represent the AUM block geometric mean labeling framework, enabling a clear mapping of symptom connections across different treatment blocks. The summary of results obtained through the Google survey link is provided in this paper.