Deep Learning Hybrid Models for COVID-19 Prediction

ABSTRACT

COVID-19 is a highly contagious virus. Blood test is one of effective method for COVID-19 diagnosis. However, the issues of blood test are time-consuming and lack of medical staffs. 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. Besides, 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, and do not have errors caused by fatigue. We can envisage a wide deployment of proposed model in hospitals to alleviate the testing pressure from medical workers, especially in developing and underdeveloped countries.