The Development of Polycystic Ovary Syndrome Risk Evaluation System using Advanced Machine Learning Technique

A complex endocrine disorder that affects fertile women, polycystic ovary syndrome, also known as PCOS, is characterized by an extensive spectrum of symptoms. This work extends previous efforts in PCOS detection and tracking with the goal of utilizing state-of-the-art machine learning techniques to overcome existing constraints. The goal of the project is to improve medical outcomes by utilizing predictive analytics to analyze big patient datasets that include genetic and clinical data. In this work, robust prediction models are developed by applying machine learning techniques, including Random Forest, decision trees, and support vector machines. These models aim to accurately identify individuals who are susceptible to PCOS and forecast the course of the disease in those who have already been diagnosed. Crucially, an extensive performance assessment is provided, contrasting the suggested models with current ones to emphasize their advantages and shortcomings. In-depth dataset investigation, rigorous model training, and comprehensive assessment metrics are conducted as part of the experiments. The outcomes highlight the usefulness of the models and indicate how they could assist medical practitioners in making defensible decisions. Predictive analytics integrated into electronic health records (EHRs) allows for ongoing PCOS patient monitoring, enabling timely interventions, individualized treatment plans, and better patient results. This research advances the area by improving the accuracy of PCOS diagnosis and therapy and by addressing the shortcomings of the previous literature. The suggested machine learning methods improve patient satisfaction, health outcomes, and system productivity, in addition to enhancing identification accuracy. This revised strategy guarantees a more impactful and targeted portrayal of the research activity, in line with the input that was given.