Predictive Modeling to Identify Syndrome Patterns

Polycystic ovarian syndrome, or PCOS, is a prevalent endocrine disorder that impacts a considerable portion of women. PCOS is regarded as a severe health issue that affects women all over the world. Women suffer significantly since they are often unaware of the effects on them or learn about them much later in life. This is a curable cause of infertility that impacts women in numerous ways, such as depression, sleep apnea, metabolic syndrome, and endometrial cancer. But if she detects it in time, these can be avoided under close monitoring. The chance of long-term effects, such as an elevated risk of type 2 diabetes and gestational diabetes, is reduced by early PCOS diagnosis and treatment. As a result, effective and early detection would mitigate the problems and complications related to PCOS, benefiting the healthcare systems. Since PCOS symptoms are rare and sometimes even differ from patient to patient, diagnosing PCOS manually is a laborious process that takes a long time and almost always involves human error. Machine Learning (ML) advances have demonstrated the potential to enhance medical diagnosis. To generate the best model and ideal feature selection, feature selection strategies that make use of a range of machine learning models—such as Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), and Logistic Regression (LR) are used. To do this, machine learning methods were used on a dataset accessible to the public in the Kaggle repository. Of the 541, 177 individuals had PCOS, and those patients’ 45 attributes were part of the dataset. The optimal features for PCOS prediction were initially identified using the univariate feature selection method. Following the ranking of the features, the most crucial factor in PCOS detection was the ratio of luteinizing hormone (LH) to follicle-stimulating hormone (FSH). Building several models is the established method of determining which model is optimal for a given dataset. Early identification and detection of PCOS can significantly enhance the condition's management and the quality of life for people with it. With a 99.60% accuracy rate using the Support Vector Machine (SVM) model, this research advances women's health by providing a functional machine-learning approach for PCOS prediction. This approach can help medical professionals identify and treat PCOS patients early, improving their quality of life.