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Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

The rising incidence of fatty liver disease (FLD) poses a health challenge, and is expected to be the leading global cause of liver-related morbidity and mortality in the near future. Early case identification is crucial for disease intervention. A retrospective cross-sectional study was performed o...

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Autores principales: Chen, Yang-Yuan, Lin, Chun-Yu, Yen, Hsu-Heng, Su, Pei-Yuan, Zeng, Ya-Huei, Huang, Siou-Ping, Liu, I-Ling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317783/
https://www.ncbi.nlm.nih.gov/pubmed/35887527
http://dx.doi.org/10.3390/jpm12071026
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author Chen, Yang-Yuan
Lin, Chun-Yu
Yen, Hsu-Heng
Su, Pei-Yuan
Zeng, Ya-Huei
Huang, Siou-Ping
Liu, I-Ling
author_facet Chen, Yang-Yuan
Lin, Chun-Yu
Yen, Hsu-Heng
Su, Pei-Yuan
Zeng, Ya-Huei
Huang, Siou-Ping
Liu, I-Ling
author_sort Chen, Yang-Yuan
collection PubMed
description The rising incidence of fatty liver disease (FLD) poses a health challenge, and is expected to be the leading global cause of liver-related morbidity and mortality in the near future. Early case identification is crucial for disease intervention. A retrospective cross-sectional study was performed on 31,930 Taiwanese subjects (25,544 training and 6386 testing sets) who had received health check-ups and abdominal ultrasounds in Changhua Christian Hospital from January 2009 to January 2019. Clinical and laboratory factors were included for analysis by different machine-learning algorithms. In addition, the performance of the machine-learning algorithms was compared with that of the fatty liver index (FLI). Totally, 6658/25,544 (26.1%) and 1647/6386 (25.8%) subjects had moderate-to-severe liver disease in the training and testing sets, respectively. Five machine-learning models were examined and demonstrated exemplary performance in predicting FLD. Among these models, the xgBoost model revealed the highest area under the receiver operating characteristic (AUROC) (0.882), accuracy (0.833), F1 score (0.829), sensitivity (0.833), and specificity (0.683) compared with those of neural network, logistic regression, random forest, and support vector machine-learning models. The xgBoost, neural network, and logistic regression models had a significantly higher AUROC than that of FLI. Body mass index was the most important feature to predict FLD according to the feature ranking scores. The xgBoost model had the best overall prediction ability for diagnosing FLD in our study. Machine-learning algorithms provide considerable benefits for screening candidates with FLD.
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spelling pubmed-93177832022-07-27 Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population Chen, Yang-Yuan Lin, Chun-Yu Yen, Hsu-Heng Su, Pei-Yuan Zeng, Ya-Huei Huang, Siou-Ping Liu, I-Ling J Pers Med Article The rising incidence of fatty liver disease (FLD) poses a health challenge, and is expected to be the leading global cause of liver-related morbidity and mortality in the near future. Early case identification is crucial for disease intervention. A retrospective cross-sectional study was performed on 31,930 Taiwanese subjects (25,544 training and 6386 testing sets) who had received health check-ups and abdominal ultrasounds in Changhua Christian Hospital from January 2009 to January 2019. Clinical and laboratory factors were included for analysis by different machine-learning algorithms. In addition, the performance of the machine-learning algorithms was compared with that of the fatty liver index (FLI). Totally, 6658/25,544 (26.1%) and 1647/6386 (25.8%) subjects had moderate-to-severe liver disease in the training and testing sets, respectively. Five machine-learning models were examined and demonstrated exemplary performance in predicting FLD. Among these models, the xgBoost model revealed the highest area under the receiver operating characteristic (AUROC) (0.882), accuracy (0.833), F1 score (0.829), sensitivity (0.833), and specificity (0.683) compared with those of neural network, logistic regression, random forest, and support vector machine-learning models. The xgBoost, neural network, and logistic regression models had a significantly higher AUROC than that of FLI. Body mass index was the most important feature to predict FLD according to the feature ranking scores. The xgBoost model had the best overall prediction ability for diagnosing FLD in our study. Machine-learning algorithms provide considerable benefits for screening candidates with FLD. MDPI 2022-06-23 /pmc/articles/PMC9317783/ /pubmed/35887527 http://dx.doi.org/10.3390/jpm12071026 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Yang-Yuan
Lin, Chun-Yu
Yen, Hsu-Heng
Su, Pei-Yuan
Zeng, Ya-Huei
Huang, Siou-Ping
Liu, I-Ling
Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population
title Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population
title_full Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population
title_fullStr Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population
title_full_unstemmed Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population
title_short Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population
title_sort machine-learning algorithm for predicting fatty liver disease in a taiwanese population
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317783/
https://www.ncbi.nlm.nih.gov/pubmed/35887527
http://dx.doi.org/10.3390/jpm12071026
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