MAFUS: a Framework to predict mortality risk in MAFLD subjects

TL;DR

MAFUS is an AI-based framework that predicts mortality risk in MAFLD patients using machine learning, with SVM as the best model and explainability analysis to understand feature contributions.

Contribution

This work introduces MAFUS, the first AI framework specifically designed to predict mortality in MAFLD subjects using diverse clinical data.

Findings

Support Vector Machines achieved highest accuracy.

Explainable AI identified key features influencing predictions.

Framework is easy to implement with available data.

Abstract

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.