Ovarian　cancer　stands　out　as　one　of　the　most　formidable　adversaries　in　women＇s　health,　largely　due　to　its　typically　subtle　and　nonspecific　early　symptoms,　which　pose　significant　challenges　to　early　detection　and　diagnosis.　Although　existing　diagnostic　methods,　such　as　biomarker　testing　and　imaging,　can　help　with　early　diagnosis　to　some　extent,　these　methods　still　have　limitations　in　sensitivity　and　accuracy,　often　leading　to　misdiagnosis　or　missed　diagnosis.　Ovarian　cancer＇s　high　heterogeneity　and　complexity　increase　diagnostic　challenges,　especially　in　disease　progression　prediction　and　patient　classification.　Machine　learning　(ML)　has　outperformed　traditional　methods　in　cancer　detection　by　processing　large　datasets　to　identify　patterns　missed　by　conventional　techniques.　However,　existing　AI　models　still　struggle　with　accuracy　in　handling　imbalanced　and　high-dimensional　data,　and　their　＂black-box＂　nature　limits　clinical　interpretability.　To　address　these　issues,　this　study　proposes　SHAP-GAN,　an　innovative　diagnostic　model　for　ovarian　cancer　that　integrates　Shapley　Additive　exPlanations　(SHAP)　with　Generative　Adversarial　Networks　(GANs).　The　SHAP　module　quantifies　each　biomarker＇s　contribution　to　the　diagnosis,　while　the　GAN　component　optimizes　medical　data　generation.　This　approach　tackles　three　key　challenges　in　medical　diagnosis:　data　scarcity,　model　interpretability,　and　diagnostic　accuracy.　Results　show　that　SHAP-GAN　outperforms　traditional　methods　in　sensitivity,　accuracy,　and　interpretability,　particularly　with　high-dimensional　and　imbalanced　ovarian　cancer　datasets.　The　top　three　influential　features　identified　are　PRR11,　CIAO1,　and　SMPD3,　which　exhibit　wide　SHAP　value　distributions,　highlighting　their　significant　impact　on　model　predictions.　The　SHAP-GAN　network　has　demonstrated　an　impressive　accuracy　rate　of　99.34%　on　the　ovarian　cancer　dataset,　significantly　outperforming　baseline　algorithms,　including　Support　Vector　Machines　(SVM),　Logistic　Regression　(LR),　and　XGBoost.　Specifically,　SVM　achieved　an　accuracy　of　72.78%,　LR　achieved　86.09%,　and　XGBoost　achieved　96.69%.　These　results　highlight　the　superior　performance　of　SHAP-GAN　in　handling　high-dimensional　and　imbalanced　datasets.　Furthermore,　SHAP-GAN　significantly　alleviates　the　challenges　associated　with　intricate　genetic　data　analysis,　empowering　medical　professionals　to　tailor　personalized　treatment　strategies　for　individual　patients.