TY - GEN
T1 - Deep Learning for COVID-19 Prediction based on Blood Test
AU - Yu, Ziyue
AU - He, Lihua
AU - Luo, Wuman
AU - Tse, Rita
AU - Pau, Giovanni
N1 - Publisher Copyright:
© 2021 by SCITEPRESS - Science and Technology Publications, Lda.
PY - 2021
Y1 - 2021
N2 - The COVID-19 pandemic is highly infectious and has caused many deaths. The COVID-19 infection diagnosis based on blood test is facing the problems of long waiting time for results and shortage of medical staff. Although several machine learning methods have been proposed to address this issue, the research of COVID-19 prediction based on deep learning is still in its preliminary stage. In this paper, we propose four hybrid deep learning models, namely CNN+GRU, CNN+Bi-RNN, CNN+Bi-LSTM and CNN+Bi-GRU, and apply them to the blood test data from Israelta Albert Einstein Hospital. We implement the four proposed models as well as other existing models CNN, CNN+LSTM, and compare them in terms of accuracy, precision, recall, F1-score and AUC. The experiment results show that CNN+Bi-GRU achieves the best performance in terms of all the five metrics (accuracy of 0.9415, F1-score of 0.9417, precision of 0.9417, recall of 0.9417, and AUC of 0.91).
AB - The COVID-19 pandemic is highly infectious and has caused many deaths. The COVID-19 infection diagnosis based on blood test is facing the problems of long waiting time for results and shortage of medical staff. Although several machine learning methods have been proposed to address this issue, the research of COVID-19 prediction based on deep learning is still in its preliminary stage. In this paper, we propose four hybrid deep learning models, namely CNN+GRU, CNN+Bi-RNN, CNN+Bi-LSTM and CNN+Bi-GRU, and apply them to the blood test data from Israelta Albert Einstein Hospital. We implement the four proposed models as well as other existing models CNN, CNN+LSTM, and compare them in terms of accuracy, precision, recall, F1-score and AUC. The experiment results show that CNN+Bi-GRU achieves the best performance in terms of all the five metrics (accuracy of 0.9415, F1-score of 0.9417, precision of 0.9417, recall of 0.9417, and AUC of 0.91).
KW - Blood Test
KW - CNN+BI-GRU
KW - Covid-19
KW - Deep Learning
UR - http://www.scopus.com/inward/record.url?scp=85137950079&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85137950079
T3 - International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings
SP - 103
EP - 111
BT - IoTBDS 2021 - Proceedings of the 6th International Conference on Internet of Things, Big Data and Security
A2 - Wills, Gary
A2 - Kacsuk, Peter
A2 - Chang, Victor
PB - Science and Technology Publications, Lda
T2 - 6th International Conference on Internet of Things, Big Data and Security, IoTBDS 2021
Y2 - 23 April 2021 through 25 April 2021
ER -