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This presentation explores the use of Explainable AI in breast cancer diagnosis, focusing on enhancing clinical decisions through transparent AI models. It details the WDBC dataset, preprocessing using StandardScaler, and performance analysis across five AI models, highlighting SVM accuracy at 98.25%. Key explainability techniques, such as SHAP, are discussed to emphasize feature contributions and their application in clinical data. The session concludes with clinical implications, key...