Comparison of Machine Learning Models and the Fatty Liver Index in Predicting Lean Fatty Liver

The reported prevalence of non-alcoholic fatty liver disease in studies of lean individuals ranges from 7.6% to 19.3%. The aim of the study was to develop machine-learning models for the prediction of fatty liver disease in lean individuals. The present retrospective study included 12,191 lean subje...

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Published in:Diagnostics (Basel) 2023-04, Vol.13 (8), p.1407
Main Authors: Su, Pei-Yuan, Chen, Yang-Yuan, Lin, Chun-Yu, Su, Wei-Wen, Huang, Siou-Ping, Yen, Hsu-Heng
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Biochemistry
Biopsy
Blood pressure
Body mass index
Chi-square test
Comparative analysis
Diagnosis
Fatty liver
fatty liver index
Feature selection
Glucose
Hemoglobin
lean fatty liver
Leukocytes
Liver cancer
Liver cirrhosis
Liver diseases
Machine learning
machine learning model
Metabolic syndrome
Neural networks
Support vector machines
Triglycerides
Ultrasonic imaging
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Summary:The reported prevalence of non-alcoholic fatty liver disease in studies of lean individuals ranges from 7.6% to 19.3%. The aim of the study was to develop machine-learning models for the prediction of fatty liver disease in lean individuals. The present retrospective study included 12,191 lean subjects with a body mass index < 23 kg/m who had undergone a health checkup from January 2009 to January 2019. Participants were divided into a training (70%, 8533 subjects) and a testing group (30%, 3568 subjects). A total of 27 clinical features were analyzed, except for medical history and history of alcohol or tobacco consumption. Among the 12,191 lean individuals included in the present study, 741 (6.1%) had fatty liver. The machine learning model comprising a two-class neural network using 10 features had the highest area under the receiver operating characteristic curve (AUROC) value (0.885) among all other algorithms. When applied to the testing group, we found the two-class neural network exhibited a slightly higher AUROC value for predicting fatty liver (0.868, 0.841-0.894) compared to the fatty liver index (FLI; 0.852, 0.824-0.81). In conclusion, the two-class neural network had greater predictive value for fatty liver than the FLI in lean individuals.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics13081407