OBJECTIVE End-stage liver disease is a global public health problem with a high mortality rate. Early identification of people at risk of poor prognosis is fundamental for decision-making in clinical settings. This study created a machine learning prediction system that provides several related models with visualized graphs, including decision trees, ensemble learning and clustering, to predict mortality in patients with end-stage liver disease. METHODS A retrospective cohort study was conducted: the training data were from patients enrolled from January 2009 to December 2010 and followed up to December 2014; validation data were from patients enrolled from January 2015 to December 2016 and followed up to January 2019. Hospitalized patients with noncancer-related chronic liver disease were identified from the hospital's electrical medical records. RESULTS In traditional multivariable logistic regression and Cox proportional hazard model, prothrombin time of international normalized ratio, which was significant with P value = 0.002, odds ratio = 2.790 and hazard ratio 1.363. Besides, blood urea nitrogen and C-reactive protein were also significant, with P value
