Explainable Machine Learning Approaches for Accurate Prediction of Polycystic Ovary Syndrome: A Review

Abstract

Polycystic Ovary Syndrome (PCOS) is a widely recognized endocrine disorder affecting women of reproductive age, causing severe complications, such as infertility, metabolic disturbances, and psychological distress. Early detection and accurate diagnosis are still challenging tasks because of heterogeneous symptoms with no specific diagnostic methods. This paper elaborated the design-development of SmartScanPCOS, a feature-based framework creatively inspired by advanced Machine Learning (ML) techniques conjugated with Explainable Artificial Intelligence (XAI) with simultaneous influence on both prediction and interpretation of a PCOS diagnosis. The paper discovered from notes critically the progress in machine-learning (ML) prediction for PCOS, basing the prediction on analytical biomarkers of hormonal levels, metabolic signal, life course markers, which generally involve diverse disease-like features; further predictive modeling takes into account medical imaging/engineering data to assess improved performance for interpretation. Several classification-based algorithms with some other major models (also compared to general linear statistical methods) within several experimental protocols plan for an effective technique; computations had to be in random forests, support vector machines, and neural networks, based on predictive comparison: Thus, some XAI methods, including LIME and SHAP, might be confirmed as interpreting the explanation by making the most influential attribute in the model decide. This is how a model provides healthcare practitioners and other services with confidence to make better decisions. The proposal, therefore, corresponds well to the issues of feature selection, model optimization, and interpretability with high class accuracy. The conclusion reached after literature review opportunities is the existence and allied collaboration of hybrid and ensemble predictors with explanation capabilities. The finding suggested possible inroads in providing some means for bridging the gap in further-processed data-driven elucidation vis-à-vis full-scale clinical considerations of PCOS diagnosis.