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Fatty Liver Disease Prediction Model Based on Big Data of Electronic Physical Examination Records

Fatty liver disease (FLD) is a common liver disease, which poses a great threat to people's health, but there is still no optimal method that can be used on a large-scale screening. This research is based on machine learning algorithms, using electronic physical examination records in the healt...

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Autores principales: Zhao, Mingqi, Song, Changjun, Luo, Tao, Huang, Tianyue, Lin, Shiming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8072129/
https://www.ncbi.nlm.nih.gov/pubmed/33912534
http://dx.doi.org/10.3389/fpubh.2021.668351
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author Zhao, Mingqi
Song, Changjun
Luo, Tao
Huang, Tianyue
Lin, Shiming
author_facet Zhao, Mingqi
Song, Changjun
Luo, Tao
Huang, Tianyue
Lin, Shiming
author_sort Zhao, Mingqi
collection PubMed
description Fatty liver disease (FLD) is a common liver disease, which poses a great threat to people's health, but there is still no optimal method that can be used on a large-scale screening. This research is based on machine learning algorithms, using electronic physical examination records in the health database as data support, to a predictive model for FLD. The model has shown good predictive ability on the test set, with its AUC reaching 0.89. Since there are a large number of electronic physical examination records in most of health database, this model might be used as a non-invasive diagnostic tool for FLD for large-scale screening.
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spelling pubmed-80721292021-04-27 Fatty Liver Disease Prediction Model Based on Big Data of Electronic Physical Examination Records Zhao, Mingqi Song, Changjun Luo, Tao Huang, Tianyue Lin, Shiming Front Public Health Public Health Fatty liver disease (FLD) is a common liver disease, which poses a great threat to people's health, but there is still no optimal method that can be used on a large-scale screening. This research is based on machine learning algorithms, using electronic physical examination records in the health database as data support, to a predictive model for FLD. The model has shown good predictive ability on the test set, with its AUC reaching 0.89. Since there are a large number of electronic physical examination records in most of health database, this model might be used as a non-invasive diagnostic tool for FLD for large-scale screening. Frontiers Media S.A. 2021-04-12 /pmc/articles/PMC8072129/ /pubmed/33912534 http://dx.doi.org/10.3389/fpubh.2021.668351 Text en Copyright © 2021 Zhao, Song, Luo, Huang and Lin. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Zhao, Mingqi
Song, Changjun
Luo, Tao
Huang, Tianyue
Lin, Shiming
Fatty Liver Disease Prediction Model Based on Big Data of Electronic Physical Examination Records
title Fatty Liver Disease Prediction Model Based on Big Data of Electronic Physical Examination Records
title_full Fatty Liver Disease Prediction Model Based on Big Data of Electronic Physical Examination Records
title_fullStr Fatty Liver Disease Prediction Model Based on Big Data of Electronic Physical Examination Records
title_full_unstemmed Fatty Liver Disease Prediction Model Based on Big Data of Electronic Physical Examination Records
title_short Fatty Liver Disease Prediction Model Based on Big Data of Electronic Physical Examination Records
title_sort fatty liver disease prediction model based on big data of electronic physical examination records
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8072129/
https://www.ncbi.nlm.nih.gov/pubmed/33912534
http://dx.doi.org/10.3389/fpubh.2021.668351
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