Machine learning approach to predict acute kidney injury after liver surgery

doi:10.12998/wjcc.v9.i36.11255

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Dec 26, 2021 (publication date) through May 18, 2024

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World Journal of Clinical Cases

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2307-8960

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Clin Cases. Dec 26, 2021; 9(36): 11255-11264
Published online Dec 26, 2021. doi: 10.12998/wjcc.v9.i36.11255

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Figure 4 Ranked variable values of the random forest algorithm. PLT: Platelet; AFP: Alpha-fetoprotein; WBC: White blood cell; BMI: Body mass index; CR: Creatinine clearance; HB: Hemoglobin; ALB: Albumin; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; SBP: Systolic blood pressure; DM: Diabetes mellitus.

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