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Evolutionary warning system for COVID-19 severity: Colony predation algorithm enhanced extreme learning machine.

Beibei Shi1, Hua Ye2, Long Zheng2

  • 1Affiliated People's Hospital of Jiangsu University, 8 Dianli Road, Zhenjiang, Jiangsu, 212000, China.

Computers in Biology and Medicine
|August 24, 2021
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Summary
This summary is machine-generated.

This study introduces an AI model using biochemical indicators for accurate COVID-19 diagnosis and severity classification. The ECPA-KELM model demonstrates improved predictive performance and stability for early disease detection and treatment.

Keywords:
Biochemical indexesCOVID-19Colony predation algorithmCoronavirus disease 2019Kernel extreme learning machineWarning system

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Healthcare
  • Computational Biology

Background:

  • The global spread of COVID-19 (Coronavirus Disease 2019) caused by SARS-CoV-2 has highlighted critical needs for early diagnosis and effective severity assessment.
  • Current diagnostic and clinical care approaches for COVID-19 face limitations, leading to high mortality rates.

Purpose of the Study:

  • To investigate the utility of biochemical indicators for discriminating and classifying COVID-19 severity using machine learning.
  • To develop an efficient artificial intelligence method for COVID-19 diagnosis based on biochemical data.

Main Methods:

  • A novel framework, ECPA-KELM, was developed by integrating an enhanced colony predation algorithm (ECPA) with a kernel extreme learning machine (KELM).
  • The ECPA algorithm incorporates operators from grey wolf and moth-flame optimizers to enhance parameter optimization and feature selection for KELM.
  • The ECPA algorithm's performance was validated using the IEEE CEC2017 benchmark dataset.

Main Results:

  • The ECPA-KELM model demonstrated superior predictive properties and enhanced stability compared to other KELM models in diagnosing COVID-19 using biochemical indexes.
  • Statistical analysis confirmed the improved performance metrics of the proposed ECPA-KELM model.

Conclusions:

  • The ECPA-KELM model shows significant potential as a computer-aided diagnostic tool for COVID-19.
  • This approach can effectively discriminate and classify COVID-19 severity, offering an early warning system for timely treatment and diagnosis.