A Critical Analysis of PCOS Detection using Machine Learning and CNN: Challenges and Future Directions

Polycystic Ovary Syndrome is an endocrine disorder that affects most of the young women. It often results in infertility and many other long-term health complications in most cases. The aim is therefore to identify it early and apply a tailored approach to treatment for effective management of PCOS. This research paper discusses the application of machine learning approaches in the detection of PCOS using varied patient data, such as hormonal levels, ultrasound images, and clinical history. The paper discusses the predictability of PCOS using some machine learning algorithms: decision trees, support vector machines, and neural networks. Challenges in the preprocessing of data and feature selection, as well as model optimization, while conducting this study are also debated. Accuracy and sensitivity of results tend to suggest that such a tool is possible to work with regarding early diagnosis and personalized treatment of PCOS.