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

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

	RFE Ranking	RF Feature Importance	Individual AUC^a
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

^aIndividual AUC score is calculated from a Logistic Regression classifier, where the feature in question is the sole independent variable