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Table 2 Feature selection by recursive feature elimination using a Random Forest Classifier. Feature importance is shown as well as the individual AUC score

From: Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections

  RFE Ranking RF Feature Importance Individual AUCa
WBC count 1 0·30 0·82
Bacterial count 1 0·30 0·71
Age 1 0·12 0·63
Epithelial cell count 1 0·07 0·49
RBC count 1 0·06 0·56
# of positive cultures to date 1 0·03 0·60
Pyuria, no RBCs 1 0·02 0·57
Pregnant 1 0·02 0·57
Inpatient 1 0·01 0·53
Gender 1 0·01 0·53
Persistent/recurrent infection 1 0·01 0·55
# of positive cultures month prior 1 0·009 0·53
Positive for nitrates 1 0·008 0·52
Renal inpatient/outpatient 1 0·005 0·50
Pre-operative patient 1 0·004 0·51
Acute kidney disease 1 0·003 0·50
Immunocompromised 2 0·002 0·50
# of positive cultures week prior 3 0·002 0·51
Multiple Sclerosis 4 0·001 0·50
Offensive smell 5 0·0007 0·50
Haematuria, no WBCs 6 0·0001 0·50
  1. aIndividual AUC score is calculated from a Logistic Regression classifier, where the feature in question is the sole independent variable