Interpretable machine learning models for beta thalassemia prediction: an explainable AI approach for smart healthcare 5.0

Furthermore ensuring the models transparency and interpretability, the proposed method integrates SHapley Ad-ditive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), enabling both global and local interpretability of model predictions. SHAP gives us insight into important features at the global level, while LIME explains individual predictions, making the model's decisions more comprehensible for clinical applications.