Fig. 2
From: Interpretable machine learning models for prolonged Emergency Department wait time prediction
Feature importance and associations from XGBoost classification model. Figure 2 illustrates the essential features contributing to wait time prediction using XGBoost algorithmic model. Panel A (Feature Importance): Feature importance of each feature contributing to the model prediction. The x-axis represents the marginal contribution of a feature to the change in the predicted probability of prolonged wait time (30min). Panel B (Feature Associations): The x-axis indicates the direction of each feature impact on the model output. SHAP values >0 indicates the prolonged wait time and <0 indicates patients wait time<30min. All features except age were dichotomous coded either 0 (no) or 1 (yes). For example, moa_ambulance (i.e., patients arrived by ambulance) had more negative values indicating the higher impact of predicting patient wait time<30min if patients arrived by ambulance