Copyright
©The Author(s) 2021.
World J Clin Cases. Dec 26, 2021; 9(36): 11255-11264
Published online Dec 26, 2021. doi: 10.12998/wjcc.v9.i36.11255
Published online Dec 26, 2021. doi: 10.12998/wjcc.v9.i36.11255
Figure 2 Tree-like algorithm.
Tree-like modelling can help analysis to reach the best prediction decision. Classification results for acute kidney injury (AKI) and non-AKI are shown in blue and orange, respectively. The smaller the Gini index, the darker the color. BMI: Body mass index; WBC: White blood cell; HGB: Hemoglobin.
- Citation: Dong JF, Xue Q, Chen T, Zhao YY, Fu H, Guo WY, Ji JS. Machine learning approach to predict acute kidney injury after liver surgery. World J Clin Cases 2021; 9(36): 11255-11264
- URL: https://www.wjgnet.com/2307-8960/full/v9/i36/11255.htm
- DOI: https://dx.doi.org/10.12998/wjcc.v9.i36.11255