Unveiling Disparities in Polycystic Ovary Syndrome Detection: A Complex Comparative Study Integrating Deep Neural Networks and Cutting-edge Data Visualization Modalities

The objective of the Polycystic Ovary Syndrome (PCOS) Detection and Analysis project is to find a solution to the ongoing problem of PCOS by employing data visualization in the form of graphs and machine learning algorithms to diagnose and investigate the condition. Polycystic ovarian syndrome (PCOS) is a multifaceted medical illness that affects the female reproductive system and has the potential to result in congenital anomalies and several other health concerns. It is difficult to diagnose and treat PCOS because its underlying cause is still unknown, even though there are medications available to treat the condition. By employing graphs and algorithms for machine learning, the project will analyze data to recognize patterns, with the goal of enhancing our understanding of PCOS and reducing the risk of further injury to those who are afflicted. The initiative aims to provide an effective solution for the diagnosis and treatment of polycystic ovarian syndrome (PCOS).