Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance.

ABSTRACT: A simple predictive biomarker for fatty liver disease is required for individuals with insulin resistance. Here, we developed a supervised machine learning-based classifier for fatty liver disease using fecal 16S rDNA sequencing data. Based on the Kangbuk Samsung Hospital cohort (n = 777), we generated a random forest classifier to predict fatty liver diseases in individuals with or without insulin resistance (n = 166 and n = 611, respectively). The model performance was evaluated based on metrics, including accuracy, area under receiver operating curve (AUROC), kappa, and F1-score. The developed classifier for fatty liver diseases performed better in individuals with insulin resistance (AUROC = 0.77). We further optimized the classifiers using genetic algorithm. The improved classifier fo

SUBMITTER: Kang BE 

PROVIDER: S-EPMC9759583 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature