PCOD Disease Detection Using Machine Learning

Background: Polycystic Ovarian Disease (PCOD) is one of the most common endocrine disorders affecting women of reproductive age, with a prevalence of 6-12% globally. Early detection and accurate diagnosis remain challenging due to the heterogeneous nature of symptoms and the complexity of diagnostic criteria. Objective: This study aims to develop and evaluate machine learning models for automated PCOD detection using clinical, biochemical, and anthropometric parameters to improve diagnostic accuracy and enable early intervention. Methods: We developed a comprehensive machine learning framework incorporating four different algorithms: Random Forest, Gradient Boosting, Logistic Regression, and Support Vector Machine. The model was trained on a dataset of 1,000 cases with 13 clinical features including hormonal profiles, metabolic parameters, and symptom assessments. Model performance was evaluated using cross-validation, ROC-AUC analysis, and comprehensive statistical metrics. Results: The Gradient Boosting model achieved the highest performance with an AUC of 0.92, sensitivity of 88.5%, and specificity of 89.2%. Feature importance analysis revealed that LH/FSH ratio, testosterone levels, and menstrual irregularity were the most significant predictors. The model demonstrated robust performance across different patient demographics. Conclusion: Machine learning models show promising potential for PCOD detection, offering a reliable, cost-effective screening tool that could enhance clinical decision-making and improve patient outcomes through early intervention.