Journal Article

Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine

Heidari, Ali Asghar, Chen, Yanfan, Huang, Xiaoying, Hu, Jiao, Shou, Yeqi, Chen, Huiling, Ye, Hua, Han, zhengyuan, Wu, Peiliang, Wang, Liangxing

2022

Overview

Coronavirus disease-2019 (COVID-19) has made the world more cautious about widespread viruses, and a tragic pandemic that was caused by a novel coronavirus has harmed human beings in recent years. The new coronavirus pneumonia outbreak is spreading rapidly worldwide. We collect arterial blood samples from 51 patients with a COVID-19 diagnosis. Blood gas analysis is performed using a Siemens RAPID Point 500 blood gas analyzer. To accurately determine the factors that play a decisive role in the early recognition and discrimination of COVID-19 severity, a prediction framework that is based on an improved binary Harris hawk optimization (HHO) algorithm in combination with a kernel extreme learning machine is proposed in this paper. This method uses specular reflection learning to improve the original HHO algorithm and is referred to as HHOSRL. The experimental results show that the selected indicators, such as age, partial pressure of oxygen, oxygen saturation, sodium ion concentration, and lactic acid, are essential for the early accurate assessment of COVID-19 severity by the proposed feature selection method. The simulation results show that the established methodlogy can achieve promising performance. We believe that our proposed model provides an effective strategy for accurate early assessment of COVID-19 and distinguishing disease severity. The codes of HHO will be updated in https://aliasgharheidari.com/HHO.html. •In this paper, specular reflection learning is combined with HHO for the first time.•An improved Harris hawk optimization (HHOSRL) is proposed to feature selection.•HHOSRL has higher classification accuracy and smaller number of features in feature selection.•HHOSRL performs well on a variety of different machine learning classifiers.

• Publisher: Elsevier Ltd,Elsevier Limited
• Subject: Algorithms / Animals / Artificial neural networks / Blood / Blood Gas Analysis / Coronavirus disease / Coronaviruses / COVID-19 / COVID-19 Testing / Extreme learning machine / Falconiformes / Feature selection / Gas analysis / Gas analyzers / Harris hawk optimization / Humans / Internal Medicine / Ion concentration / Lactic acid / Learning algorithms / Machine Learning / Neural networks / Optimization / Other / Oxygen / Oxygen content / Pandemics / Partial pressure / SARS-CoV-2 / Specular reflection / Support vector machines / Viral diseases