An effective machine learning approach for identifying non-severe and severe coronavirus disease 2019 patients in a rural Chinese population: The wenzhou retrospective study

10.1109/access.2021.3067311

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Bibliographic Details
Main Authors:	Wu, Peiliang, Ye, Hua, Cai, Xueding, Li, Chengye, Li, Shimin, Chen, Mengxiang, Wang, Mingjing, Heidari, Ali Asghar, Chen, Mayun, Li, Jifa, Chen, Huiling, Huang, Xiaoying, Wang, Liangxing
Other Authors:	COMPUTER SCIENCE
Format:	Article
Published:	Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:	Coronavirus COVID-19 Disease diagnosis Feature selection Slime mould algorithm Support vector machine
Online Access:	https://scholarbank.nus.edu.sg/handle/10635/232662
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Institution:	National University of Singapore

id	sg-nus-scholar.10635-232662
record_format	dspace
spelling	sg-nus-scholar.10635-2326622024-10-24T18:30:11Z An effective machine learning approach for identifying non-severe and severe coronavirus disease 2019 patients in a rural Chinese population: The wenzhou retrospective study Wu, Peiliang Ye, Hua Cai, Xueding Li, Chengye Li, Shimin Chen, Mengxiang Wang, Mingjing Heidari, Ali Asghar Chen, Mayun Li, Jifa Chen, Huiling Huang, Xiaoying Wang, Liangxing COMPUTER SCIENCE Coronavirus COVID-19 Disease diagnosis Feature selection Slime mould algorithm Support vector machine 10.1109/access.2021.3067311 IEEE Access 9 45486-45503 2022-10-13T00:54:44Z 2022-10-13T00:54:44Z 2021-01-01 Article Wu, Peiliang, Ye, Hua, Cai, Xueding, Li, Chengye, Li, Shimin, Chen, Mengxiang, Wang, Mingjing, Heidari, Ali Asghar, Chen, Mayun, Li, Jifa, Chen, Huiling, Huang, Xiaoying, Wang, Liangxing (2021-01-01). An effective machine learning approach for identifying non-severe and severe coronavirus disease 2019 patients in a rural Chinese population: The wenzhou retrospective study. IEEE Access 9 : 45486-45503. ScholarBank@NUS Repository. https://doi.org/10.1109/access.2021.3067311 2169-3536 https://scholarbank.nus.edu.sg/handle/10635/232662 Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ Institute of Electrical and Electronics Engineers Inc. Scopus OA2021
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
topic	Coronavirus COVID-19 Disease diagnosis Feature selection Slime mould algorithm Support vector machine
spellingShingle	Coronavirus COVID-19 Disease diagnosis Feature selection Slime mould algorithm Support vector machine Wu, Peiliang Ye, Hua Cai, Xueding Li, Chengye Li, Shimin Chen, Mengxiang Wang, Mingjing Heidari, Ali Asghar Chen, Mayun Li, Jifa Chen, Huiling Huang, Xiaoying Wang, Liangxing An effective machine learning approach for identifying non-severe and severe coronavirus disease 2019 patients in a rural Chinese population: The wenzhou retrospective study
description	10.1109/access.2021.3067311
author2	COMPUTER SCIENCE
author_facet	COMPUTER SCIENCE Wu, Peiliang Ye, Hua Cai, Xueding Li, Chengye Li, Shimin Chen, Mengxiang Wang, Mingjing Heidari, Ali Asghar Chen, Mayun Li, Jifa Chen, Huiling Huang, Xiaoying Wang, Liangxing
format	Article
author	Wu, Peiliang Ye, Hua Cai, Xueding Li, Chengye Li, Shimin Chen, Mengxiang Wang, Mingjing Heidari, Ali Asghar Chen, Mayun Li, Jifa Chen, Huiling Huang, Xiaoying Wang, Liangxing
author_sort	Wu, Peiliang
title	An effective machine learning approach for identifying non-severe and severe coronavirus disease 2019 patients in a rural Chinese population: The wenzhou retrospective study
title_short	An effective machine learning approach for identifying non-severe and severe coronavirus disease 2019 patients in a rural Chinese population: The wenzhou retrospective study
title_full	An effective machine learning approach for identifying non-severe and severe coronavirus disease 2019 patients in a rural Chinese population: The wenzhou retrospective study
title_fullStr	An effective machine learning approach for identifying non-severe and severe coronavirus disease 2019 patients in a rural Chinese population: The wenzhou retrospective study
title_full_unstemmed	An effective machine learning approach for identifying non-severe and severe coronavirus disease 2019 patients in a rural Chinese population: The wenzhou retrospective study
title_sort	effective machine learning approach for identifying non-severe and severe coronavirus disease 2019 patients in a rural chinese population: the wenzhou retrospective study
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2022
url	https://scholarbank.nus.edu.sg/handle/10635/232662
_version_	1821195239373668352

An effective machine learning approach for identifying non-severe and severe coronavirus disease 2019 patients in a rural Chinese population: The wenzhou retrospective study

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