Fig. 3
From: Prediction of the development of acute kidney injury following cardiac surgery by machine learning
Simple decision tree model illustrating the classification of patients with (class = yes) and without (class = no) acute kidney injury. Each box has the following components: selected variables for classification, Gini index, number of samples classified to the box according to the previous variable, the average number of patients for each classification with 5-cross validation, and the majority of classes at the split node. Blue and orange represent the yes class and the no class, respectively, and the color densities increase when the Gini indexes decrease. Abbreviations: pRBC, packed red blood cell; BMI, body mass index; CCS, Canadian Cardiovascular Society; LV, left ventricular; HGB, hemoglobin