MAFUS: a Framework to predict mortality risk in MAFLD subjects

• URL: http://arxiv.org/abs/2301.06908v1
• Date: Tue, 17 Jan 2023 14:19:51 GMT
• Title: MAFUS: a Framework to predict mortality risk in MAFLD subjects
• Authors: Domenico Lof\`u, Paolo Sorino, Tommaso Colafiglio, Caterina Bonfiglio, Fedelucio Narducci, Tommaso Di Noia and Eugenio Di Sciascio
• Abstract summary: We propose an artificial intelligence-based framework that can predict mortality in Metabolic (dysfunction) associated fatty liver disease (MAFLD) subjects. The framework uses data from various anthropometric and biochemical sources based on Machine Learning (ML) algorithms.
• Score: 7.418913276983601
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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