Figure 3
From: Early risk assessment for COVID-19 patients from emergency department data using machine learning
Feature importance for AICU admission. (A–C) Permutation feature importance for the logistic regression (A), random forest (B) and XGBoost (C) models. Only the top 15 features are shown. Asterisks mark features with importance scores significantly different from zero across three cross-validation folds with t-test p value thresholds of 5% ( ∗) and 1% (∗ ∗). (D–F) Accumulated local effects plots for the logistic regression (D), random forest (E) and XGBoost models (F). The top two features according to permutation feature importance are shown for each model. Vertical bars at the bottom indicate feature values observed in the data set.
