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...
Ausführliche Beschreibung
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] |
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E-Artikel |
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Sprache: |
Englisch |
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2023 |
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Übergeordnetes Werk: |
In: Diagnostics - MDPI AG, 2012, 13(2023), 8, p 1407 |
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Übergeordnetes Werk: |
volume:13 ; year:2023 ; number:8, p 1407 |
Links: |
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DOI / URN: |
10.3390/diagnostics13081407 |
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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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10.3390/diagnostics13081407 doi (DE-627)DOAJ089875745 (DE-599)DOAJ4d3e0e0a971f4313a4673229e4fe0e2c DE-627 ger DE-627 rakwb eng R5-920 Pei-Yuan Su verfasserin aut Comparison of Machine Learning Models and the Fatty Liver Index in Predicting Lean Fatty Liver 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. lean fatty liver machine learning model fatty liver index Medicine (General) Yang-Yuan Chen verfasserin aut Chun-Yu Lin verfasserin aut Wei-Wen Su verfasserin aut Siou-Ping Huang verfasserin aut Hsu-Heng Yen verfasserin aut In Diagnostics MDPI AG, 2012 13(2023), 8, p 1407 (DE-627)718627814 (DE-600)2662336-5 20754418 nnns volume:13 year:2023 number:8, p 1407 https://doi.org/10.3390/diagnostics13081407 kostenfrei https://doaj.org/article/4d3e0e0a971f4313a4673229e4fe0e2c kostenfrei https://www.mdpi.com/2075-4418/13/8/1407 kostenfrei https://doaj.org/toc/2075-4418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 8, p 1407 |
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10.3390/diagnostics13081407 doi (DE-627)DOAJ089875745 (DE-599)DOAJ4d3e0e0a971f4313a4673229e4fe0e2c DE-627 ger DE-627 rakwb eng R5-920 Pei-Yuan Su verfasserin aut Comparison of Machine Learning Models and the Fatty Liver Index in Predicting Lean Fatty Liver 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. lean fatty liver machine learning model fatty liver index Medicine (General) Yang-Yuan Chen verfasserin aut Chun-Yu Lin verfasserin aut Wei-Wen Su verfasserin aut Siou-Ping Huang verfasserin aut Hsu-Heng Yen verfasserin aut In Diagnostics MDPI AG, 2012 13(2023), 8, p 1407 (DE-627)718627814 (DE-600)2662336-5 20754418 nnns volume:13 year:2023 number:8, p 1407 https://doi.org/10.3390/diagnostics13081407 kostenfrei https://doaj.org/article/4d3e0e0a971f4313a4673229e4fe0e2c kostenfrei https://www.mdpi.com/2075-4418/13/8/1407 kostenfrei https://doaj.org/toc/2075-4418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 8, p 1407 |
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10.3390/diagnostics13081407 doi (DE-627)DOAJ089875745 (DE-599)DOAJ4d3e0e0a971f4313a4673229e4fe0e2c DE-627 ger DE-627 rakwb eng R5-920 Pei-Yuan Su verfasserin aut Comparison of Machine Learning Models and the Fatty Liver Index in Predicting Lean Fatty Liver 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. lean fatty liver machine learning model fatty liver index Medicine (General) Yang-Yuan Chen verfasserin aut Chun-Yu Lin verfasserin aut Wei-Wen Su verfasserin aut Siou-Ping Huang verfasserin aut Hsu-Heng Yen verfasserin aut In Diagnostics MDPI AG, 2012 13(2023), 8, p 1407 (DE-627)718627814 (DE-600)2662336-5 20754418 nnns volume:13 year:2023 number:8, p 1407 https://doi.org/10.3390/diagnostics13081407 kostenfrei https://doaj.org/article/4d3e0e0a971f4313a4673229e4fe0e2c kostenfrei https://www.mdpi.com/2075-4418/13/8/1407 kostenfrei https://doaj.org/toc/2075-4418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 8, p 1407 |
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10.3390/diagnostics13081407 doi (DE-627)DOAJ089875745 (DE-599)DOAJ4d3e0e0a971f4313a4673229e4fe0e2c DE-627 ger DE-627 rakwb eng R5-920 Pei-Yuan Su verfasserin aut Comparison of Machine Learning Models and the Fatty Liver Index in Predicting Lean Fatty Liver 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. lean fatty liver machine learning model fatty liver index Medicine (General) Yang-Yuan Chen verfasserin aut Chun-Yu Lin verfasserin aut Wei-Wen Su verfasserin aut Siou-Ping Huang verfasserin aut Hsu-Heng Yen verfasserin aut In Diagnostics MDPI AG, 2012 13(2023), 8, p 1407 (DE-627)718627814 (DE-600)2662336-5 20754418 nnns volume:13 year:2023 number:8, p 1407 https://doi.org/10.3390/diagnostics13081407 kostenfrei https://doaj.org/article/4d3e0e0a971f4313a4673229e4fe0e2c kostenfrei https://www.mdpi.com/2075-4418/13/8/1407 kostenfrei https://doaj.org/toc/2075-4418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 8, p 1407 |
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Comparison of Machine Learning Models and the Fatty Liver Index in Predicting Lean Fatty Liver |
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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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