Performance of machine learning models on HF dataset using 5-fold cross validation.

Models are referred to as transparent if they can explain their recommendations in a way understood by humans. The column ‘Interpretability’ indicates whether the feature importance can be provided with the model. Although Random Forest, XGBoost and SVM are listed as interpretable, these models can only be interpreted using external approach such as SHAP (SHapley Additive exPlanations). The column “Rules” refers to whether the model provides a set of clinical rules by which to explain its prediction.