SHAP-based interpretability provided transparent, class-specific insights into model predictions by highlighting key biomarkers and risk factors, such as ARID1A loss, elevated CA125, thrombocytosis, p16 expression, FIGO stage, LVSI, cytology, tumor size, and E-cadherin status. This evidence concerns the gene ARID1A and thrombocytosis disease.