Approach for training quantum neural network to predict severity of COVID-19 in patients

Engy El-Shafeiy, Aboul Ella Hassanien, Karam M. Sallam, A. A. Abohany

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1745-1755
Number of pages11
JournalComputers, Materials and Continua
Volume66
Issue number2
DOIs
Publication statusPublished - 26 Nov 2020
Externally publishedYes

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