Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jul 19;10(7):1339.
doi: 10.3390/healthcare10071339.

Enhanced Gravitational Search Optimization with Hybrid Deep Learning Model for COVID-19 Diagnosis on Epidemiology Data

Mahmoud Ragab  1   2   3 , Hani Choudhry  2   4 , Amer H Asseri  2   4 , Sami Saeed Binyamin  5 , Mohammed W Al-Rabia  6   7
Affiliations

Enhanced Gravitational Search Optimization with Hybrid Deep Learning Model for COVID-19 Diagnosis on Epidemiology Data

Mahmoud Ragab et al. Healthcare (Basel). .

Abstract

Effective screening provides efficient and quick diagnoses of COVID-19 and could alleviate related problems in the health care system. A prediction model that combines multiple features to assess contamination risks was established in the hope of supporting healthcare workers worldwide in triaging patients, particularly in situations with limited health care resources. Furthermore, a lack of diagnosis kits and asymptomatic cases can lead to missed or delayed diagnoses, exposing visitors, medical staff, and patients to 2019-nCoV contamination. Non-clinical techniques including data mining, expert systems, machine learning, and other artificial intelligence technologies have a crucial role to play in containment and diagnosis in the COVID-19 outbreak. This study developed Enhanced Gravitational Search Optimization with a Hybrid Deep Learning Model (EGSO-HDLM) for COVID-19 diagnoses using epidemiology data. The major aim of designing the EGSO-HDLM model was the identification and classification of COVID-19 using epidemiology data. In order to examine the epidemiology data, the EGSO-HDLM model employed a hybrid convolutional neural network with a gated recurrent unit based fusion (HCNN-GRUF) model. In addition, the hyperparameter optimization of the HCNN-GRUF model was improved by the use of the EGSO algorithm, which was derived by including the concepts of cat map and the traditional GSO algorithm. The design of the EGSO algorithm helps in reducing the ergodic problem, avoiding premature convergence, and enhancing algorithm efficiency. To demonstrate the better performance of the EGSO-HDLM model, experimental validation on a benchmark dataset was performed. The simulation results ensured the enhanced performance of the EGSO-HDLM model over recent approaches.

Keywords: COVID-19; disease detection; epidemiology data; fusion model; health promotion; hybrid deep learning; parameter optimization.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interests.

Figures

Figure 1
Figure 1
Overall process of EGSO-HDLM technique.
Figure 2
Figure 2
Structure of GRU Model.
Figure 3
Figure 3
Confusion matrices of EGSO-HDLM algorithm (a) 70% of TR; (b) 30% of TS; (c) 80% of TR; (d) 20% of TS data.
Figure 4
Figure 4
Result analysis of EGSO-HDLM approach under 70% of TR data.
Figure 5
Figure 5
Result analysis of EGSO-HDLM approach under 30% of TS data.
Figure 6
Figure 6
Result analysis of EGSO-HDLM approach under 80% of TR data.
Figure 7
Figure 7
Result analysis of EGSO-HDLM approach under 20% of TS data.
Figure 8
Figure 8
TA and VA analysis of EGSO-HDLM methodology.
Figure 9
Figure 9
TL and VL analysis of EGSO-HDLM methodology.
Figure 10
Figure 10
Precision-recall curve analysis of EGSO-HDLM methodology.
Figure 11
Figure 11
ROC curve analysis of EGSO-HDLM methodology.
Figure 12
Figure 12
Accuy analysis of EGSO-HDLM algorithm with existing methodologies.
Figure 13
Figure 13
Precn analysis of EGSO-HDLM algorithm with existing methodologies.
Figure 14
Figure 14
Recal analysis of EGSO-HDLM algorithm with existing methodologies.
See this image and copyright information in PMC

References

    1. Shuja J., Alanazi E., Alasmary W., Alashaikh A. COVID-19 open source data sets: A comprehensive survey. Appl. Intell. 2021;51:1296–1325. doi: 10.1007/s10489-020-01862-6. - DOI - PMC - PubMed
    1. Yuki K., Fujiogi M., Koutsogiannaki S. COVID-19 pathophysiology: A review. Clin. Immunol. 2020;215:108427. doi: 10.1016/j.clim.2020.108427. - DOI - PMC - PubMed
    1. Rockett R.J., Arnott A., Lam C., Sadsad R., Timms V., Gray K.A., Eden J.S., Chang S., Gall M., Draper J., et al. Revealing COVID-19 transmission in Australia by SARS-CoV-2 genome sequencing and agent-based modeling. Nat. Med. 2020;26:1398–1404. doi: 10.1038/s41591-020-1000-7. - DOI - PubMed
    1. Böhmer M.M., Buchholz U., Corman V.M., Hoch M., Katz K., Marosevic D.V., Böhm S., Woudenberg T., Ackermann N., Konrad R., et al. Investigation of a COVID-19 outbreak in Germany resulting from a single travel-associated primary case: A case series. Lancet Infect. Dis. 2020;20:920–928. doi: 10.1016/S1473-3099(20)30314-5. - DOI - PMC - PubMed
    1. Pan A., Liu L., Wang C., Guo H., Hao X., Wang Q., Huang J., He N., Yu H., Lin X., et al. Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China. JAMA. 2020;323:1915–1923. doi: 10.1001/jama.2020.6130. - DOI - PMC - PubMed

LinkOut - more resources