Model Interpretation
EVENT
The black box characteristic of ML models makes it difficult to interpret how an ML algorithm could perform accurate prediction in clinical medicine settings. Accordingly, the Shapley additive explanations (SHAP) value was introduced in this study. SHAP was used to interpret the results from a predictive model. The interpretation was based on the SHAP value for each feature, representing the contribution of a feature to the predicted risk of the event. A positive SHAP value indicated that the corresponding feature contributed to a higher risk of the event, whereas a negative SHAP value indicated that the corresponding feature led to a lower risk of the event. The magnitude of SHAP values represented the contribution of that feature toward prediction performance [