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Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest.

Rafael García-Carretero^1,2, Roberto Holgado-Cuadrado¹, Óscar Barquero-Pérez¹

¹Department of Signal Theory and Communications and Telematics Systems and Computing, Rey Juan Carlos University, 28935 Mostoles, Spain.

Entropy (Basel, Switzerland)

|July 2, 2021

Summary

This summary is machine-generated.

Machine learning accurately predicts nonalcoholic steatohepatitis (NASH) risk by analyzing key features like insulin resistance and triglycerides. This approach enhances understanding of disease progression and patient stratification for metabolic syndrome.

Keywords:

interpretability non-alcoholic fatty liver disease random forest

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Area of Science:

Hepatology
Medical Informatics
Metabolic Syndrome Research

Background:

Nonalcoholic fatty liver disease (NAFLD) is a prevalent chronic liver condition linked to metabolic syndrome.
Progression to nonalcoholic steatohepatitis (NASH), marked by inflammation and liver damage, is influenced by factors like insulin resistance and dyslipidemia.

Purpose of the Study:

To evaluate the utility of machine learning, specifically random forest (RF) models, for predicting NASH risk.
To identify and interpret the most significant predictive features for NASH development using machine learning.

Main Methods:

Collected data from 1525 patients at a Cardiovascular Risk Unit (2005-2021).
Developed six RF models with varying pre-processing strategies to predict NASH based on electronic health records.
Employed interpretability techniques, including feature importance and partial dependence plots, to analyze model behavior.

Main Results:

The best RF model achieved an accuracy of 0.87 in classifying patients with NASH.
Key predictive features identified were insulin resistance, ferritin, serum insulin levels, and triglycerides.
Filter methods did not provide significant insights into variable interactions.

Conclusions:

Machine learning, particularly RF, offers a robust method for predicting NASH risk.
The study highlights the importance of insulin resistance, ferritin, insulin, and triglycerides in NASH development.
ML interpretability techniques enhance understanding of disease predictors and aid clinical decision-making.