Sustainable Practices in Enhanced Diagnosis and Monitoring of Polycystic Ovary Syndrome (PCOS) using Machine Learning

Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder affecting a significant percentage of women of reproductive age, leading to various metabolic, reproductive, and psychological issues. Despite its prevalence, the diagnosis and monitoring of PCOS remain challenging due to its heterogeneous nature and the multifactorial aspects of its manifestation. This study presents the development and implementation of advanced machine learning techniques aimed at enhancing the diagnosis and monitoring of PCOS, leveraging predictive analytics to improve medical outcomes. Introduced a comprehensive framework that integrates diverse data sources, including clinical, hormonal, and ultrasound data, along with patient lifestyle and genetic information. Advanced machine learning models including Random Forest, Decision Trees, and Logistic Regression were employed and compared to identify the most effective approach for PCOS diagnosis and prognosis. The models were trained, validated, and tested on a large dataset obtained from various healthcare facilities, ensuring a robust and generalizable system. The results demonstrate the potential of machine learning in revolutionizing the management of PCOS. The models achieved high accuracy, sensitivity, and specificity in diagnosing PCOS and predicting associated complications, such as infertility, metabolic syndrome, and cardiovascular risks. Furthermore, the system provides personalized treatment and lifestyle recommendations, contributing to a holistic approach to managing PCOS.