Improving the diagnostic performance of the neural network for COVID-19VN via weight assignment to pathological symptoms

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Authors

  • Le Minh Ngoc Department of Science and Technology Management, Academy of Military Science and Technology
  • Nguyen Thi Thuy (Corresponding Author) Faculty of Electronics and Telecommunications, Electric Power University
  • Dinh Van Quang National Hospital For Tropical Diseases
  • Dinh Van Dai National Hospital For Tropical Diseases
  • Bui Van Tan National Hospital For Tropical Diseases

DOI:

https://doi.org/10.54939/1859-1043.j.mst.95.2024.29-37

Keywords:

Artificial neural networks; DATABASE Coronavirus disease (COVID-19); MultiLayer Perceptron (MLP); Assigning weight.

Abstract

In this article, we present how to create a database of Covid-19 diseases at National Hospital of Tropical Diseases (called the CovidVN database) and then develop a learning neural network based on this database to diagnose this disease. The CovidVN Database is built based on the processing of real diagnostic test results of Covid-19 patients with a large number of samples and in accordance with the structure of the Israeli Health System Covid-19 disease database (called COVIDIsr Database). Then two MLP Artificial Neural Networks corresponding to these two databases will be developed using the Deep Learning Toolbox of MATLAB software; the results of training these networks and their accuracy are compared with each other to assess the relative quality of the CovidVN database. Other, the paper presents the method of assigning weight corresponding to pathological symptoms for network input parameters. The results showed that the weighting of input attributes corresponding to the pathological symptoms is significant.

References

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Published

20-05-2024

How to Cite

Le Minh, N., T. Nguyen, Q. Đinh Van, Đại Đinh Van, and T. Bùi Văn. “Improving the Diagnostic Performance of the Neural Network for COVID-19VN via Weight Assignment to Pathological Symptoms”. Journal of Military Science and Technology, vol. 95, no. 95, May 2024, pp. 29-37, doi:10.54939/1859-1043.j.mst.95.2024.29-37.

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