Health Science Reports (Jan 2023)

Evaluation of effective features in the diagnosis of Covid‐19 infection from routine blood tests with multilayer perceptron neural network: A cross‐sectional study

  • Fatemeh Mohammadi,
  • Leila Dehbozorgi,
  • Hamid Reza Akbari‐Hasanjani,
  • Zahra Joz Abbasalian,
  • Reza Akbari‐Hasanjani,
  • Reza Sabbaghi‐Nadooshan,
  • Hedieh Moradi Tabriz

DOI
https://doi.org/10.1002/hsr2.1048
Journal volume & issue
Vol. 6, no. 1
pp. n/a – n/a

Abstract

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Abstract Background and Aim Coronavirus is an infectious disease that is now known as an epidemic, early and accurate diagnosis helps the patient receive more care. The aim of this study is to investigate Covid‐19 using blood tests and multilayer perceptron neural network and affective factors in improving and preventing Covid‐19. Methods This cross‐sectional study was performed on 200 patients referred to Sina Hospital, Tehran, Iran, who were confirmed cases of Covid‐19 by computerized tomography‐scan analysis between 2 March 2020 to 5 April 2020. After verification of lung involvement, blood sampling was done to separate the sera for C‐reactive protein (CRP), magnesium (Mg), lymphocyte percentage, and vitamin D analysis in healthy and unhealthy people. Blood samples from healthy and sick people were applied to the multilayer perceptron network for 70% of the data for training and 30% for testing. Result By examining the features, it was found that in patients with Covid‐19, there was a significant relationship between increased CRP and decreased lymphocyte levels, and increased Mg (p < 0.01). In these patients, the amount of CRP and Mg in women and the number of lymphocytes and vitamin D in men were significantly higher (p < 0.01). Conclusion The important advantage of using a multilayer perceptron neural network is to speed up the diagnosis and treatment.

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