Identification of hepatic steatosis in living liver donors by machine learning models.

ABSTRACT: Selecting an optimal donor for living donor liver transplantation is crucial for the safety of both the donor and recipient, and hepatic steatosis is an important consideration. We aimed to build a prediction model with noninvasive variables to evaluate macrovesicular steatosis in potential donors by using various prediction models. The study population comprised potential living donors who had undergone donation workup, including percutaneous liver biopsy, in the Republic of Korea between 2016 and 2019. Meaningful macrovesicular hepatic steatosis was defined as >5%. Whole data were divided into training (70.5%) and test (29.5%) data sets based on the date of liver biopsy. Random forest, support vector machine, regularized discriminant analysis, mixture discriminant analysis, flexible disc

SUBMITTER: Lim J 

PROVIDER: S-EPMC9234640 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature