Ovulytics: A Machine Learning Approach for Precision Diagnosis of PCOD, PCOD and Infertility

Polycystic Ovary Syndrome (PCDS) and Polycystic Ovarian Disease (PCOD) are common endocrine disorders that affect a significant percentage of women of reproductive age, often leading to complications such as infertility. Early detection and differentiation of these conditions are important for timely intervention and effective management. This research proposes a machine learning-based solution to classify and predict the occurrence of PCDS, PCOD, and PCDS with infertility using Random Forest, Logistic Regression, and XGBoost. Each algorithm is trained and tested to predict the level of presence of PCDS, PCOD, and infertility-related PCDS. Among the models, Random Forest demonstrates superior performance. However, the importance of our work extends beyond performance metrics. This study illustrates how machine learning can be applied to complex, real-world healthcare challenges, such as early diagnosis and differentiation of overlapping conditions like PCDS and PCOD. The ability to leverage machine learning in this context aids earlier detection of these conditions, allowing for timely interventions that can have the potential to prevent further complications, such as infertility, cardiovascular diseases, and metabolic disorders. The potential impact of this research is substantial, offering scalable, non-invasive, and cost-effective diagnostic solutions, thus improving outcomes for millions of women worldwide and transforming reproductive healthcare practices.