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Stacked deep learning approach for efficient SARS-CoV-2 detection in blood samples.

Wu Wang1, Fouzi Harrou2, Abdelkader Dairi3

  • 1Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing 100872, China.

Artificial Intelligence in Medicine
|February 7, 2024
PubMed
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This study introduces a novel stacked deep learning approach for detecting COVID-19 from blood samples, improving accuracy and speed over current methods. The StackMax algorithm demonstrated superior performance in identifying infected individuals.

Area of Science:

  • Computational biology
  • Medical diagnostics
  • Machine learning applications

Background:

  • Current COVID-19 blood tests require specialized labs and have a significant false-negative rate.
  • Timely and accurate detection of COVID-19 is critical for patient management and outcomes.

Purpose of the Study:

  • To develop and evaluate a stacked deep learning approach for COVID-19 detection in blood samples.
  • To improve the accuracy and efficiency of identifying infected individuals compared to existing methods.

Main Methods:

  • Three stacked deep learning architectures (StackMean, StackMax, StackRF) were proposed.
  • Synthetic Minority Oversampling Technique (SMOTE) was used to address class imbalance.
  • Performance was validated using blood samples from Brazil and Italy.
Keywords:
Blood test samplesCOVID-19Deep learningStacked modelsUnbalanced data

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Main Results:

  • The StackMax method significantly enhanced the ability to distinguish COVID-19 positive cases.
  • SMOTE improved the specificity and sensitivity of the stacked models.
  • XGBoost analysis identified key blood sample features for COVID-19 detection.

Conclusions:

  • The proposed stacked deep learning methodology offers a promising advancement for timely and precise COVID-19 identification from blood samples.
  • This approach has the potential to overcome limitations of current diagnostic methods.