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Approach for Training Quantum Neural Network to Predict Severity of COVID-19 in Patients

Engy El-shafeiy1, Aboul Ella Hassanien2, Karam M. Sallam3,*, A. A. Abohany4

1 Department of Computer Science, Faculty of Computers and Artificial Intelligence, University of Sadat City, Sadat City, 32897, Egypt
2 Faculty of Computers and Artificial Intelligence, Cairo University, 12613, Egypt
3 Faculty of Computers and Information, Zagazig University, 44519, Egypt
4 Faculty of Computers and Information, Kafrelsheikh University, 33516, Egypt

* Corresponding Author: Karam M. Sallam. Email: email

(This article belongs to this Special Issue: Security and Computing in Internet of Things)

Computers, Materials & Continua 2021, 66(2), 1745-1755.


Currently, COVID-19 is spreading all over the world and profoundly impacting people’s lives and economic activities. In this paper, a novel approach called the COVID-19 Quantum Neural Network (CQNN) for predicting the severity of COVID-19 in patients is proposed. It consists of two phases: In the first, the most distinct subset of features in a dataset is identified using a Quick Reduct Feature Selection (QRFS) method to improve its classification performance; and, in the second, machine learning is used to train the quantum neural network to classify the risk. It is found that patients’ serial blood counts (their numbers of lymphocytes from days 1 to 15 after admission to hospital) are associated with relapse rates and evaluations of COVID-19 infections. Accordingly, the severity of COVID-19 is classified in two categories, serious and non-serious. The experimental results indicate that the proposed CQNN’s prediction approach outperforms those of other classification algorithms and its high accuracy confirms its effectiveness.


Cite This Article

E. El-shafeiy, A. Ella Hassanien, K. M. Sallam and A. A. Abohany, "Approach for training quantum neural network to predict severity of covid-19 in patients," Computers, Materials & Continua, vol. 66, no.2, pp. 1745–1755, 2021.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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