Retrospective Study
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Hepatol. Oct 27, 2021; 13(10): 1417-1427
Published online Oct 27, 2021. doi: 10.4254/wjh.v13.i10.1417
Machine learning models for predicting non-alcoholic fatty liver disease in the general United States population: NHANES database
Amporn Atsawarungruangkit, Passisd Laoveeravat, Kittichai Promrat
Amporn Atsawarungruangkit, Kittichai Promrat, Division of Gastroenterology, Warren Alpert Medical School, Brown University, Providence, RI 02903, United States
Passisd Laoveeravat, Division of Digestive Diseases and Nutrition, University of Kentucky College of Medicine, Lexington, KY 40536, United States
Kittichai Promrat, Division of Gastroenterology and Hepatology, Providence VA Medical Center, Providence, RI 02908, United States
Author contributions: Atsawarungruangkit A and Laoveeravat P contributed equally to this work including study design, data analysis, result interpretation, and manuscript writing; Promrat K critically revised the manuscript and provided supervision.
Institutional review board statement: The National Health and Nutrition Examination Survey protocol was approved by the National Center for Health Statistics Research Ethics Review Board (Hyattsville, MD, United States).
Informed consent statement: In NHANES III, the consent form was signed by participants in the survey.
Conflict-of-interest statement: No conflict of interest exists.
Data sharing statement: The dataset used in this manuscript is NHANES III, which is publicly available dataset.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Amporn Atsawarungruangkit, MD, Academic Fellow, Instructor, Research Fellow, Division of Gastroenterology, Warren Alpert Medical School, Brown University, 593 Eddy Street, POB 240, Providence, RI 02903, United States. amporn_atsawarungruangkit@brown.edu
Received: March 7, 2021
Peer-review started: March 7, 2021
First decision: May 2, 2021
Revised: May 11, 2021
Accepted: September 19, 2021
Article in press: September 19, 2021
Published online: October 27, 2021
Processing time: 229 Days and 11.1 Hours
ARTICLE HIGHLIGHTS
Research background

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease that can progress to more severe liver disease.

Research motivation

Early patient identification using a simple method is highly desirable for preventing the progression of NAFLD.

Research objectives

To create machine learning models for predicting NAFLD in the general United States population.

Research methods

This study was designed as a retrospective cohort by using the NHANES 1988-1994. Adults (20 years and above in age) with gradable ultrasound results were included in this study.

Research results

Based on F1, the ensemble of ensemble of random undersampling boosted trees was the top performer (accuracy 71.1% and F1 0.56) while a simple model (coarse trees) had an accuracy of 74.9% and an F1 of 0.33.

Research conclusions

Although a simpler model such as coarse trees was not the top performer, it consisted of only two predictors: fasting C-peptide and waist circumference. Its simplicity is useful in clinical practice.

Research perspectives

The findings from this study can facilitate clinical decision-making for clinicians and also allow researchers to investigate the developed machine learning models. This will lead to proper investigation and treatment selection for specific individuals at risk, helping to maximize healthcare resource utilization.