CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Developing an artificial neural network for detecting COVID-19 disease
Authors
H. Kazemi-Arpanahi
R. Nopour
M. Shanbehzadeh
Publication date
1 January 2022
Publisher
'Medknow'
Doi
View
on
PubMed
Abstract
BACKGROUND: From December 2019, atypical pneumonia termed COVID-19 has been increasing exponentially across the world. It poses a great threat and challenge to world health and the economy. Medical specialists face uncertainty in making decisions based on their judgment for COVID-19. Thus, this study aimed to establish an intelligent model based on artificial neural networks (ANNs) for diagnosing COVID-19. MATERIALS AND METHODS: Using a single-center registry, we studied the records of 250 confirmed COVID-19 and 150 negative cases from February 9, 2020, to October 20, 2020. The correlation coefficient technique was used to determine the most significant variables of the ANN model. The variables at P < 0.05 were used for model construction. We applied the back-propagation technique for training a neural network on the dataset. After comparing different neural network configurations, the best configuration of ANN was acquired, then its strength has been evaluated. RESULTS: After the feature selection process, a total of 18 variables were determined as the most relevant predictors for developing the ANN models. The results indicated that two nested loops' architecture of 9-10-15-2 (10 and 15 neurons used in layer 1 and layer 2, respectively) with the area under the curve of 0.982, the sensitivity of 96.4, specificity of 90.6, and accuracy of 94 was introduced as the best configuration model for COVID-19 diagnosis. CONCLUSION: The proposed ANN-based clinical decision support system could be considered as a suitable computational technique for the frontline practitioner in early detection, effective intervention, and possibly a reduction of mortality in patients with COVID-19. © 2022 Journal of Education and Health Promotion
Similar works
Full text
Available Versions
PubMed Central
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:pubmedcentral.nih.gov:9277...
Last time updated on 30/08/2022
Research Repository Portal of Medilam
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:eprints.medilam.ac.ir:3877
Last time updated on 02/08/2022
Research Repository Portal of Medilam
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:eprints.medilam.ac.ir:4079
Last time updated on 24/10/2022
Research Repository Portal of Medilam
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:eprints.medilam.ac.ir:4211
Last time updated on 15/05/2023