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Diagnosis of COVID-19 through blood sample using ensemble genetic algorithms and machine learning classifier

Rumi Iqbal Doewes (Faculty of Sport, Universitas Sebelas Maret, Surakarta, Indonesia)
Rajit Nair (School of Engineering and Technology, Jagran Lakecity university, Bhopal, India)
Tripti Sharma (Department of Information Technology, Maharaja Surajmal Institute of Technology, New Delhi, India)

World Journal of Engineering

ISSN: 1708-5284

Article publication date: 1 July 2021

Issue publication date: 15 March 2022

166

Abstract

Purpose

This purpose of this study is to perfrom the analysis of COVID-19 with the help of blood samples. The blood samples used in the study consist of more than 100 features. So to process high dimensional data, feature reduction has been performed by using the genetic algorithm.

Design/methodology/approach

In this study, the authors will implement the genetic algorithm for the prediction of COVID-19 from the blood test sample. The sample contains records of around 5,644 patients with 111 attributes. The genetic algorithm such as relief with ant colony optimization algorithm will be used for dimensionality reduction approach.

Findings

The implementation of this study is done through python programming language and the performance evaluation of the model is done through various parameters such as accuracy, sensitivity, specificity and area under curve (AUC).

Originality/value

The implemented model has achieved an accuracy of 98.7%, sensitivity of 96.76%, specificity of 98.80% and AUC of 92%. The results have shown that the implemented algorithm has performed better than other states of the art algorithms.

Keywords

Acknowledgements

The authors are very thankful to our colleagues for their encouragement and motivation to write and implement the proposed work. The author has not recieved any funding to complete this work.

Citation

Doewes, R.I., Nair, R. and Sharma, T. (2022), "Diagnosis of COVID-19 through blood sample using ensemble genetic algorithms and machine learning classifier", World Journal of Engineering, Vol. 19 No. 2, pp. 175-182. https://doi.org/10.1108/WJE-03-2021-0174

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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