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Machine Learning Approach for Confirmation of COVID-19 Cases: Positive, Negative, Death and Release — Europe PMC PPR:PPR130530

Source: Europe PMC
Original: https://europepmc.org/article/PPR/PPR130530
DOI: 10.1101/2020.03.25.20043505
License (as archived): CC0-1.0
Archived: 2026-11-09T03:48:28.580Z
SHA-256 of the archived text: 76168e998138777e5cf653f142ee13e07c530b95bb40333a778c5844d5fe16da
Stable URL of this page: https://metraipolis.com/documento/dc_73fefb2f41da

How to cite

Machine Learning Approach for Confirmation of COVID-19 Cases: Positive, Negative, Death and Release — Europe PMC PPR:PPR130530 — Europe PMC, https://europepmc.org/article/PPR/PPR130530 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_73fefb2f41da (Metraipolis archive dc_73fefb2f41da, sha256 76168e998138777e5cf653f142ee13e07c530b95bb40333a778c5844d5fe16da)

BibTeX

@misc{dc73fefb2f41da,
  title = {Machine Learning Approach for Confirmation of COVID-19 Cases: Positive, Negative, Death and Release — Europe PMC PPR:PPR130530},
  howpublished = {Europe PMC},
  url = {https://europepmc.org/article/PPR/PPR130530},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_73fefb2f41da; sha256 76168e998138777e5cf653f142ee13e07c530b95bb40333a778c5844d5fe16da},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: Europe PMC, https://europepmc.org/article/PPR/PPR130530. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In recent days, Covid-19 coronavirus has been an immense impact on social, economic fields in the world. The objective of this study determines if it is feasible to use machine learning method to evaluate how much prediction results are close to original data related to Confirmed-Negative-Released-Death cases of Covid-19. For this purpose, a verification method is proposed in this paper that uses the concept of Deep-learning Neural Network. In this framework, Long short-term memory (LSTM) and Gated Recurrent Unit (GRU) are also assimilated finally for training the dataset and the prediction results are tally with the results predicted by clinical doctors. The prediction results are validated against the original data based on some predefined metric. The experimental results showcase that the proposed approach is useful in generating suitable results based on the critical disease outbreak. It also helps doctors to recheck further verification of virus by the proposed method. 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 with in 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. It could be a promising supplementary confirmation method for frontline clinical doctors. The proposed method has a high prediction rate and works fast for probable accurate identification of the disease. The performance analysis shows that a high rate of accuracy is obtained by the proposed method.

Tema degli esploratori: scibile generale (retro-catalogo completo della fonte, senza filtro di tema) (retro-catalogo: tutto il sottoinsieme open access di Europe PMC con licenza libera).
Pubblicazione: medRxiv, 2020 (prima pubblicazione 2020-03-30).
Identificativi: DOI 10.1101/2020.03.25.20043505.
Pagina: https://europepmc.org/article/PPR/PPR130530 — https://doi.org/10.1101/2020.03.25.20043505. Autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza dell'articolo dichiarata da Europe PMC: CC0 (la versione è indicata dall'editore sulla pagina). Abstract riportato senza modifiche al contenuto, con attribuzione; deposito marcato CC0.
Letto il 2026-11-09T03:44:36.784Z tramite l'API REST ufficiale di Europe PMC (EMBL-EBI), nel rispetto del robots.txt e del crawl-delay.