Prediction of the Severity of Covid-19 Patients based on Demographics, Comorbidities and Symptoms using Backpropagation Neural

DOI

10.2991/978-94-6463-525-6_41How to use a DOI?

Keywords

COVID-19; demographics; artificial neural network; complaint; comorbid

Abstract

Coronavirus is a stranded RNA virus. Coronaviruses belong to the Coronavirdiae family, which infects birds, mammals, and humans, among others. Since the initial report of pneumonia in Wuhan, China in December 2019, SARS-CoV-2 has spread to more than 200 countries and has become a global pandemic. Given that the vast majority of patients who show symptoms related to Covid-19 infection end up negative, there is concern that large numbers of uninfected individuals may come into contact with infected patients in testing centers or emergency departments. Therefore, overcrowded hospitals and clinics can present ideal conditions for virus transmission. Thus, minimizing overcrowding in hospital waiting rooms and clinics is essential to reduce nosocomial spread. The purpose of the study is to provide information in the form of predictions of the severity of Covid-19 patients based on information that can be collected, including demographic data, comorbidities, and complaints suffered by using backpropagation neural networks. From the research that has been done, it can be concluded that the backpropagation artificial neural network using the Gradient Descent optimization method provides better performance than the backpropagation artificial neural network using the Fletcher-Revees optimization method, although the difference in results between the two is not too significant.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Indah Yanti
AU  - Syaiful Anam
AU  - Zuraidah Fitriah
AU  - Aurick Yudha Nagara
PY  - 2024
DA  - 2024/10/29
TI  - Prediction of the Severity of Covid-19 Patients based on Demographics, Comorbidities and Symptoms using Backpropagation Neural
BT  - Proceedings of the 2023 Brawijaya International Conference (BIC 2023)
PB  - Atlantis Press
SP  - 357
EP  - 363
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-525-6_41
DO  - 10.2991/978-94-6463-525-6_41
ID  - Yanti2024
ER  -