MAFUS: a Framework to predict mortality risk in MAFLD subjects
This work addresses a critical gap in predicting fatal outcomes for MAFLD patients, offering a practical tool for physicians, though it appears incremental as it applies existing ML methods to a new dataset in this domain.
The authors tackled the problem of predicting mortality risk in patients with Metabolic (dysfunction) Associated Fatty Liver Disease (MAFLD) by proposing an AI-based framework called MAFUS, which uses machine learning algorithms on anthropometric and biochemical data, with Support Vector Machines identified as the best-performing model and an explainable AI analysis to interpret feature contributions.
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.