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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Autor*in:	Pei-Yuan Su [verfasserIn] Yang-Yuan Chen [verfasserIn] Chun-Yu Lin [verfasserIn] Wei-Wen Su [verfasserIn] Siou-Ping Huang [verfasserIn] Hsu-Heng Yen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	lean fatty liver machine learning model fatty liver index

Übergeordnetes Werk:	In: Diagnostics - MDPI AG, 2012, 13(2023), 8, p 1407
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:8, p 1407

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/diagnostics13081407

Katalog-ID:	DOAJ089875745

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520			\|a 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<sup<2</sup< 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.
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language	English
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authorswithroles_txt_mv	Pei-Yuan Su @@aut@@ Yang-Yuan Chen @@aut@@ Chun-Yu Lin @@aut@@ Wei-Wen Su @@aut@@ Siou-Ping Huang @@aut@@ Hsu-Heng Yen @@aut@@
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callnumber-first	R - Medicine
author	Pei-Yuan Su
spellingShingle	Pei-Yuan Su misc R5-920 misc lean fatty liver misc machine learning model misc fatty liver index misc Medicine (General) Comparison of Machine Learning Models and the Fatty Liver Index in Predicting Lean Fatty Liver
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topic_title	R5-920 Comparison of Machine Learning Models and the Fatty Liver Index in Predicting Lean Fatty Liver lean fatty liver machine learning model fatty liver index
topic	misc R5-920 misc lean fatty liver misc machine learning model misc fatty liver index misc Medicine (General)
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title	Comparison of Machine Learning Models and the Fatty Liver Index in Predicting Lean Fatty Liver
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title_full	Comparison of Machine Learning Models and the Fatty Liver Index in Predicting Lean Fatty Liver
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author_browse	Pei-Yuan Su Yang-Yuan Chen Chun-Yu Lin Wei-Wen Su Siou-Ping Huang Hsu-Heng Yen
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title_sort	comparison of machine learning models and the fatty liver index in predicting lean fatty liver
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title_auth	Comparison of Machine Learning Models and the Fatty Liver Index in Predicting Lean Fatty Liver
abstract	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<sup<2</sup< 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.
abstractGer	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<sup<2</sup< 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.
abstract_unstemmed	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<sup<2</sup< 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.
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title_short	Comparison of Machine Learning Models and the Fatty Liver Index in Predicting Lean Fatty Liver
url	https://doi.org/10.3390/diagnostics13081407 https://doaj.org/article/4d3e0e0a971f4313a4673229e4fe0e2c https://www.mdpi.com/2075-4418/13/8/1407 https://doaj.org/toc/2075-4418
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Comparison of Machine Learning Models and the Fatty Liver Index in Predicting Lean Fatty Liver

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