Predictive Algorithms for PCOS: Harnessing Data to Uncover Hidden Risks

Polycystic Ovary Syndrome (PCOS) is a highly common issue but largely ignored hormonal disorder that affects females in their reproductive age. The varied and shifting symptoms it brings sometimes make it very difficult to identify and diagnose on time. This research paper examines the use of predictive instruments with special interest in algorithms of machine learning of the ensemble type in the determination of PCOS and the presentation of hidden risk factors. Our proposed workflow is end to end implementation of data cleaning, model training, feature engineering assessment model evaluation and explainability holiness. The results indicate that best results were achieved with XGBoost algorithm as it achieved 94 percent and a 0.97 AUC-ROC score, significantly higher than traditional algorithms. Moreover, SHAP and LIME interpretation methods provided meaningful clinical insights, which are more self-assuring and open to AI-dependent diagnostic systems. On balance, it can be seen that our solution demonstrates a great potential of early PCOS detection and a tailored care plan.