High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis

Abstract Chronic HBV infection, the main cause of liver cirrhosis and hepatocellular carcinoma, has become a global health concern. Machine learning algorithms are particularly adept at analyzing medical phenomenon by capturing complex and nonlinear relationships in clinical data. Our study proposed...

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Autores principales:	Xiangke Pu, Danni Deng, Chaoyi Chu, Tianle Zhou, Jianhong Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b5985c689bca4d44b5ba92b2d83e075f
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spelling	oai:doaj.org-article:b5985c689bca4d44b5ba92b2d83e075f2021-12-02T13:30:51ZHigh-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis10.1038/s41598-021-84556-42045-2322https://doaj.org/article/b5985c689bca4d44b5ba92b2d83e075f2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84556-4https://doaj.org/toc/2045-2322Abstract Chronic HBV infection, the main cause of liver cirrhosis and hepatocellular carcinoma, has become a global health concern. Machine learning algorithms are particularly adept at analyzing medical phenomenon by capturing complex and nonlinear relationships in clinical data. Our study proposed a predictive model on the basis of 55 routine laboratory and clinical parameters by machine learning algorithms as a novel non-invasive method for liver fibrosis diagnosis. The model was further evaluated on the accuracy and rationality and proved to be highly accurate and efficient for the prediction of HBV-related fibrosis. In conclusion, we suggested a potential combination of high-dimensional clinical data and machine learning predictive algorithms for the liver fibrosis diagnosis.Xiangke PuDanni DengChaoyi ChuTianle ZhouJianhong LiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xiangke Pu Danni Deng Chaoyi Chu Tianle Zhou Jianhong Liu High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis
description	Abstract Chronic HBV infection, the main cause of liver cirrhosis and hepatocellular carcinoma, has become a global health concern. Machine learning algorithms are particularly adept at analyzing medical phenomenon by capturing complex and nonlinear relationships in clinical data. Our study proposed a predictive model on the basis of 55 routine laboratory and clinical parameters by machine learning algorithms as a novel non-invasive method for liver fibrosis diagnosis. The model was further evaluated on the accuracy and rationality and proved to be highly accurate and efficient for the prediction of HBV-related fibrosis. In conclusion, we suggested a potential combination of high-dimensional clinical data and machine learning predictive algorithms for the liver fibrosis diagnosis.
format	article
author	Xiangke Pu Danni Deng Chaoyi Chu Tianle Zhou Jianhong Liu
author_facet	Xiangke Pu Danni Deng Chaoyi Chu Tianle Zhou Jianhong Liu
author_sort	Xiangke Pu
title	High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis
title_short	High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis
title_full	High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis
title_fullStr	High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis
title_full_unstemmed	High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis
title_sort	high-dimensional hepatopath data analysis by machine learning for predicting hbv-related fibrosis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b5985c689bca4d44b5ba92b2d83e075f
work_keys_str_mv	AT xiangkepu highdimensionalhepatopathdataanalysisbymachinelearningforpredictinghbvrelatedfibrosis AT dannideng highdimensionalhepatopathdataanalysisbymachinelearningforpredictinghbvrelatedfibrosis AT chaoyichu highdimensionalhepatopathdataanalysisbymachinelearningforpredictinghbvrelatedfibrosis AT tianlezhou highdimensionalhepatopathdataanalysisbymachinelearningforpredictinghbvrelatedfibrosis AT jianhongliu highdimensionalhepatopathdataanalysisbymachinelearningforpredictinghbvrelatedfibrosis
_version_	1718392937667100672

High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis

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