MAFUS: a Framework to predict mortality risk in MAFLD subjects

Domenico Lofù, Paolo Sorino, Tommaso Colafiglio, Caterina Bonfiglio, Fedelucio Narducci +2 more
1/17/2023
stat.MLcs.LG

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Feature-Enhanced Machine Learning for All-Cause Mortality Prediction in Healthcare Data

82% match2025

AI-Driven Non-Invasive Detection and Staging of Steatosis in Fatty Liver Disease Using a Novel Cascade Model and Information Fusion Techniques

81% match2024

Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Methods

81% match2025

Multi-modal Learning based Prediction for Disease

81% match2023

Liver Infection Prediction Analysis using Machine Learning to Evaluate Analytical Performance in Neural Networks by Optimization Techniques

79% match2023

Early Prediction of Liver Cirrhosis Up to Three Years in Advance: A Machine Learning Study Benchmarking Against the FIB-4 Score

79% match2026

Cite this paper

@article{lof2023mafus,
  title  = {MAFUS: a Framework to predict mortality risk in MAFLD subjects},
  author = {Domenico Lofù and Paolo Sorino and Tommaso Colafiglio and Caterina Bonfiglio and Fedelucio Narducci and Tommaso Di Noia and Eugenio Di Sciascio},
  year   = {2023},
  eprint = {2301.06908},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2301.06908v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2301.06908.svg)](https://codeofpaper.com/paper/2301.06908)

Discussion