Machine learning to predict progression of non-alcoholic fatty liver to non-alcoholic steatohepatitis or fibrosis.

ABSTRACT:

Background

Non-alcoholic fatty liver (NAFL) can progress to the severe subtype non-alcoholic steatohepatitis (NASH) and/or fibrosis, which are associated with increased morbidity, mortality, and healthcare costs. Current machine learning studies detect NASH; however, this study is unique in predicting the progression of NAFL patients to NASH or fibrosis.

Aim

To utilize clinical information from NAFL-diagnosed patients to predict the likelihood of progression to NASH or fibrosis.

Methods

Data were collected from electronic health records of patients receiving a first-time NAFL diagnosis. A gradient boosted machine learning algorithm (XGBoost) as well as logistic regression (LR) and multi-layer perceptron (MLP) models were developed. A five-fold cross-validation grid se

SUBMITTER: Ghandian S 

PROVIDER: S-EPMC8938756 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature