  Metabolic (dysfunction) associated fatty liver disease (MAFLD) establishes
new criteria for diagnosing fatty liver disease independent of alcohol
consumption and concurrent viral hepatitis infection. However, the long-term
outcome of MAFLD subjects is sparse. Few articles are focused on mortality in
MAFLD subjects, and none investigate how to predict a fatal outcome. In this
paper, we propose an artificial intelligence-based framework named MAFUS that
physicians can use for predicting mortality in MAFLD subjects. The framework
uses data from various anthropometric and biochemical sources based on Machine
Learning (ML) algorithms. The framework has been tested on a state-of-the-art
dataset on which five ML algorithms are trained. Support Vector Machines
resulted in being the best model. Furthermore, an Explainable Artificial
Intelligence (XAI) analysis has been performed to understand the SVM diagnostic
reasoning and the contribution of each feature to the prediction. The MAFUS
framework is easy to apply, and the required parameters are readily available
in the dataset.
