Deep Learning Hybrid Models for COVID-19 Prediction

Ziyue Yu, Lihua He, Wuman Luo, Rita Tse, Giovanni Pau

Source Title: Journal of Global Information Management (JGIM)30(10)

ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781668457047|DOI: 10.4018/JGIM.302890

MLA

Yu, Ziyue, et al. "Deep Learning Hybrid Models for COVID-19 Prediction." JGIM vol.30, no.10 2022: pp.1-20. http://doi.org/10.4018/JGIM.302890

APA

Yu, Z., He, L., Luo, W., Tse, R., & Pau, G. (2022). Deep Learning Hybrid Models for COVID-19 Prediction. Journal of Global Information Management (JGIM), 30(10), 1-20. http://doi.org/10.4018/JGIM.302890

Chicago

Yu, Ziyue, et al. "Deep Learning Hybrid Models for COVID-19 Prediction," Journal of Global Information Management (JGIM) 30, no.10: 1-20. http://doi.org/10.4018/JGIM.302890

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Abstract

COVID-19 is a highly contagious virus. Blood test is one of effective methods for COVID-19 diagnosis. However, the issues of blood test are time-consuming and lack of medical staff. In this paper, four deep learning hybrid models are proposed to address these issues (i.e., CNN+GRU, CNN+Bi-RNN, CNN+Bi-LSTM, CNN+Bi-GRU). In addition, two best models, CNN and CNN+LSTM, from Turabieh et al. and Alakus et al., are implemented, respectively. Blood test data from Hospital Israelita Albert Einstein is used to train and test six models. The proposed best model, CNN+Bi-GRU, is accuracy of 0.9415, precision of 0.9417, recall of 0.9417, F1-score of 0.9417, AUC of 0.91, which outperforms the best models from Turabieh et al. and Alakus et al. Furthermore, the proposed model can help patients to get blood test results faster than traditional manual tests without errors caused by fatigue. The authors can envisage a wide deployment of proposed model in hospitals to alleviate the testing pressure from medical workers, especially in developing and underdeveloped countries.