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Identification and classification of coronavirus genomic signals based on linear predictive coding and machine

Amin Khodaei1, Parvaneh Shams2, Hadi Sharifi1

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Summary

This study introduces a novel pattern recognition model using signal processing and linear predictive coding to accurately distinguish COVID-19 from influenza. The model achieved over 98% accuracy, aiding in the digital management of medical big data.

Keywords:
CoronaDNA SequenceLinear predictive codingMachine learningSignal processingSupport vector machine

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Area of Science:

  • Medical informatics
  • Signal processing
  • Machine learning

Background:

  • Clinical symptoms of Corona disease (COVID-19) often mimic other infectious viral illnesses like the common cold and influenza, complicating diagnosis.
  • The diagnostic challenges hinder effective coping and treatment strategies for COVID-19.
  • Understanding the genetic structure and origin of the virus is crucial for developing accurate diagnostic tools.

Purpose of the Study:

  • To investigate the origin and genetic structure of the virus causing COVID-19.
  • To develop and validate a pattern recognition model for differentiating COVID-19 from influenza.
  • To assess the model's accuracy and potential for digitizing medical big data.

Main Methods:

  • Application of signal processing and linear predictive coding techniques, commonly used in data compression.
  • Development of a pattern recognition model based on a support vector machine classifier.
  • Testing the model on diverse datasets collected internationally to evaluate its generalizability.

Main Results:

  • The proposed model successfully detected and separated COVID-19 samples from influenza cases across multiple datasets.
  • The model demonstrated high performance, achieving over 98% accuracy on all tested datasets.
  • The findings indicate the model's robustness and effectiveness in distinguishing between the two viral infections.

Conclusions:

  • The developed pattern recognition model offers a highly accurate method for differentiating COVID-19 from influenza.
  • This approach represents a significant advancement in the digital quantification and management of medical big data.
  • The model's success can contribute to improved diagnostic capabilities and public health responses during viral epidemics.