A two‐stage neural network prediction of chronic kidney disease

Abstract Accurate detection of chronic kidney disease (CKD) plays a pivotal role in early diagnosis and treatment. Measured glomerular filtration rate (mGFR) is considered the benchmark indicator in measuring the kidney function. However, due to the high resource cost of measuring mGFR, it is usuall...

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Autores principales:	Hongquan Peng, Haibin Zhu, Chi Wa Ao Ieong, Tao Tao, Tsung Yang Tsai, Zhi Liu
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/2bc7a33df5da469f899c68063f4b7664
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spelling	oai:doaj.org-article:2bc7a33df5da469f899c68063f4b76642021-11-05T12:35:39ZA two‐stage neural network prediction of chronic kidney disease1751-88571751-884910.1049/syb2.12031https://doaj.org/article/2bc7a33df5da469f899c68063f4b76642021-09-01T00:00:00Zhttps://doi.org/10.1049/syb2.12031https://doaj.org/toc/1751-8849https://doaj.org/toc/1751-8857Abstract Accurate detection of chronic kidney disease (CKD) plays a pivotal role in early diagnosis and treatment. Measured glomerular filtration rate (mGFR) is considered the benchmark indicator in measuring the kidney function. However, due to the high resource cost of measuring mGFR, it is usually approximated by the estimated glomerular filtration rate, underscoring an urgent need for more precise and stable approaches. With the introduction of novel machine learning methodologies, prediction performance is shown to be significantly improved across all available data, but the performance is still limited because of the lack of models in dealing with ultra‐high dimensional datasets. This study aims to provide a two‐stage neural network approach for prediction of GFR and to suggest some other useful biomarkers obtained from the blood metabolites in measuring GFR. It is a composite of feature shrinkage and neural network when the number of features is much larger than the number of training samples. The results show that the proposed method outperforms the existing ones, such as convolutionneural network and direct deep neural network.Hongquan PengHaibin ZhuChi Wa Ao IeongTao TaoTsung Yang TsaiZhi LiuWileyarticleBiology (General)QH301-705.5ENIET Systems Biology, Vol 15, Iss 5, Pp 163-171 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Hongquan Peng Haibin Zhu Chi Wa Ao Ieong Tao Tao Tsung Yang Tsai Zhi Liu A two‐stage neural network prediction of chronic kidney disease
description	Abstract Accurate detection of chronic kidney disease (CKD) plays a pivotal role in early diagnosis and treatment. Measured glomerular filtration rate (mGFR) is considered the benchmark indicator in measuring the kidney function. However, due to the high resource cost of measuring mGFR, it is usually approximated by the estimated glomerular filtration rate, underscoring an urgent need for more precise and stable approaches. With the introduction of novel machine learning methodologies, prediction performance is shown to be significantly improved across all available data, but the performance is still limited because of the lack of models in dealing with ultra‐high dimensional datasets. This study aims to provide a two‐stage neural network approach for prediction of GFR and to suggest some other useful biomarkers obtained from the blood metabolites in measuring GFR. It is a composite of feature shrinkage and neural network when the number of features is much larger than the number of training samples. The results show that the proposed method outperforms the existing ones, such as convolutionneural network and direct deep neural network.
format	article
author	Hongquan Peng Haibin Zhu Chi Wa Ao Ieong Tao Tao Tsung Yang Tsai Zhi Liu
author_facet	Hongquan Peng Haibin Zhu Chi Wa Ao Ieong Tao Tao Tsung Yang Tsai Zhi Liu
author_sort	Hongquan Peng
title	A two‐stage neural network prediction of chronic kidney disease
title_short	A two‐stage neural network prediction of chronic kidney disease
title_full	A two‐stage neural network prediction of chronic kidney disease
title_fullStr	A two‐stage neural network prediction of chronic kidney disease
title_full_unstemmed	A two‐stage neural network prediction of chronic kidney disease
title_sort	two‐stage neural network prediction of chronic kidney disease
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/2bc7a33df5da469f899c68063f4b7664
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_version_	1718444278889316352

A two‐stage neural network prediction of chronic kidney disease

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