Fig. 3From: Development of a personalized diagnostic model for kidney stone disease tailored to acute care by integrating large clinical, demographics and laboratory data: the diagnostic acute care algorithm - kidney stones (DACA-KS)Model comparison via AUROC. Legend: Left panels: kidney stone (KS) formers vs. other genitourinary diseases (GUD); middle panels: KS vs. other non-genitourinary (OTH) conditions; right panels: KS vs. acute localized pain (ALP) in the abdomen, back, flank, or groin. Top panels: logistic regression models upon stepwise feature selection, fit on selected input domains; Bottom panels: comparison of machine learning techniques on the full input set. Curves shown are averaged over 10-fold cross-validation, i.e. using the test setsBack to article page