Fig. 3: Importance of variables in a machine learning prediction model. | npj Digital Medicine

Fig. 3: Importance of variables in a machine learning prediction model.

From: Development and evaluation of a machine learning model predicting out-of-hospital cardiac arrest using environmental factors

Fig. 3: Importance of variables in a machine learning prediction model.

This figure shows a variable importance plot for meteorological variables (red), chronological variables (blue), and sociodemographic variables (black) in a machine learning prediction model using XGBoost. The yellow to purple dots in each row represent low to high values for each predictor normally scaled. The x-axis shows the Shapley value, indicating the variable’s impact on the model. Positive SHAP values tend to drive predictions toward more cases of OHCA and negative SHAP values tend to drive the prediction toward fewer cases of OHCA. * In the model, 2013 was considered year 0. OHCA denotes out-of-hospital cardiac arrest; SHAP, Shapley Additive Explanations; XGBoost, eXtreme Gradient Boosting.

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