Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release

Introduction: Corona Virus Infectious Disease (COVID-19) is the infectious disease. The COVID-19 disease came to earth in early 2019. It is expanding exponentially throughout the world and affected an enormous number of human beings starting from the last month. The World Health Organization (WHO) o...

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Autores principales:	Shawni Dutta, Samir Kumar Bandyopadhyay
Formato:	article
Lenguaje:	EN
Publicado:	Emergency Department of Hospital San Pedro (Logroño, Spain) 2020
Materias:	machine learning lstm gru rnn covid-19 Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/87a5cfe219174ec4904701b0121954d6
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spelling	oai:doaj.org-article:87a5cfe219174ec4904701b0121954d62021-12-02T18:12:17ZMachine learning approach for confirmation of COVID-19 cases: positive, negative, death and release10.5281/zenodo.38226232695-5075https://doaj.org/article/87a5cfe219174ec4904701b0121954d62020-05-01T00:00:00Zhttps://doi.org/10.5281/zenodo.3822623https://doaj.org/toc/2695-5075Introduction: Corona Virus Infectious Disease (COVID-19) is the infectious disease. The COVID-19 disease came to earth in early 2019. It is expanding exponentially throughout the world and affected an enormous number of human beings starting from the last month. The World Health Organization (WHO) on March 11, 2020 declared COVID-19 was characterized as “Pandemic”. This paper proposed approach for confirmation of COVID-19 cases after the diagnosis of doctors. The objective of this study uses machine learning method to evaluate how much predicted results are close to original data related to Confirmed-Negative-Released-Death cases of COVID-19. Materials and methods: For this purpose, a verification method is proposed in this paper that uses the concept of Deep-learning Neural Network. In this framework, Long shrt-term memory (LSTM) and Gated Recurrent Unit (GRU) are also assimilated finally for training the dataset. The prediction results are tally with the results predicted by clinical doctors. Results: The results are obtained from the proposed method with accuracy 87 % for the “confirmed Cases”, 67.8 % for “Negative Cases”, 62% for “Deceased Case” and 40.5 % for “Released Case”. Another important parameter i.e. RMSE shows 30.15% for Confirmed Case, 49.4 % for Negative Cases, 4.16 % for Deceased Case and 13.72 % for Released Case. Conclusions: The outbreak of Coronavirus has the nature of exponential growth and so it is difficult to control with limited clinical persons for handling a huge number of patients within a reasonable time. So it is necessary to build an automated model, based on machine learning approach, for corrective measure after the decision of clinical doctors.Shawni DuttaSamir Kumar BandyopadhyayEmergency Department of Hospital San Pedro (Logroño, Spain)articlemachine learninglstmgrurnncovid-19Medicine (General)R5-920ENIberoamerican Journal of Medicine, Vol 2, Iss 3, Pp 172-177 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning lstm gru rnn covid-19 Medicine (General) R5-920
spellingShingle	machine learning lstm gru rnn covid-19 Medicine (General) R5-920 Shawni Dutta Samir Kumar Bandyopadhyay Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release
description	Introduction: Corona Virus Infectious Disease (COVID-19) is the infectious disease. The COVID-19 disease came to earth in early 2019. It is expanding exponentially throughout the world and affected an enormous number of human beings starting from the last month. The World Health Organization (WHO) on March 11, 2020 declared COVID-19 was characterized as “Pandemic”. This paper proposed approach for confirmation of COVID-19 cases after the diagnosis of doctors. The objective of this study uses machine learning method to evaluate how much predicted results are close to original data related to Confirmed-Negative-Released-Death cases of COVID-19. Materials and methods: For this purpose, a verification method is proposed in this paper that uses the concept of Deep-learning Neural Network. In this framework, Long shrt-term memory (LSTM) and Gated Recurrent Unit (GRU) are also assimilated finally for training the dataset. The prediction results are tally with the results predicted by clinical doctors. Results: The results are obtained from the proposed method with accuracy 87 % for the “confirmed Cases”, 67.8 % for “Negative Cases”, 62% for “Deceased Case” and 40.5 % for “Released Case”. Another important parameter i.e. RMSE shows 30.15% for Confirmed Case, 49.4 % for Negative Cases, 4.16 % for Deceased Case and 13.72 % for Released Case. Conclusions: The outbreak of Coronavirus has the nature of exponential growth and so it is difficult to control with limited clinical persons for handling a huge number of patients within a reasonable time. So it is necessary to build an automated model, based on machine learning approach, for corrective measure after the decision of clinical doctors.
format	article
author	Shawni Dutta Samir Kumar Bandyopadhyay
author_facet	Shawni Dutta Samir Kumar Bandyopadhyay
author_sort	Shawni Dutta
title	Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release
title_short	Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release
title_full	Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release
title_fullStr	Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release
title_full_unstemmed	Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release
title_sort	machine learning approach for confirmation of covid-19 cases: positive, negative, death and release
publisher	Emergency Department of Hospital San Pedro (Logroño, Spain)
publishDate	2020
url	https://doaj.org/article/87a5cfe219174ec4904701b0121954d6
work_keys_str_mv	AT shawnidutta machinelearningapproachforconfirmationofcovid19casespositivenegativedeathandrelease AT samirkumarbandyopadhyay machinelearningapproachforconfirmationofcovid19casespositivenegativedeathandrelease
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Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release

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