Fig. 2

The importance of each feature cluster was summarized as radar plots based on the frequency (range: 0–1) of resulting in a > 0.01 decline in AUC score across four machine learning (ML) models (logistic regression, decision tree, random forest, and extreme gradient boosting) for each cohort. The larger the chart area, the more important the feature cluster was across all cohorts (0.25 = important in one ML model, 0.50 = important in two ML models, 0.75 = important in three ML models, 1 = important for all four ML models)