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A Precise Method to Detect Post-COVID-19 Pulmonary Fibrosis Through Extreme Gradient Boosting.

Manika Jha1, Richa Gupta1, Rajiv Saxena1

  • 1Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, 201309 Noida, India.

SN Computer Science
|December 19, 2022
PubMed
Summary

This study introduces an ensemble machine learning model using XGBoost to detect pulmonary fibrosis in COVID-19 survivors. The approach accurately identifies patients at risk, aiding early intervention for severe COVID-19 complications.

Keywords:
COVID-19Clinical decision supportExtreme gradient boostingMachine learningMedical diagnosisPulmonary fibrosisTree boosting

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Radiology

Background:

  • COVID-19 is linked to pulmonary fibrosis, a significant cause of mortality.
  • Early detection of post-COVID-19 pulmonary fibrosis is critical for patient outcomes.
  • Automatic disease detection systems assist clinicians in achieving rapid and accurate diagnoses.

Purpose of the Study:

  • To propose an ensemble machine learning architecture for detecting COVID-19-associated pulmonary fibrosis.
  • To optimize Extreme Gradient Boosting (XGBoost) for predicting severe COVID-19 patients who develop pulmonary fibrosis post-discharge.
  • To leverage Electronic Health Records (EHR) and High-resolution computed tomography (HRCT) data for risk analysis.

Main Methods:

  • Utilized an ensemble machine learning approach, specifically focusing on Extreme Gradient Boosting (XGBoost).
  • Tuned hyperparameters of the XGBoost model to enhance predictive performance.
  • Analyzed a dataset of 1175 COVID-19 patients, including EHR and HRCT scans, with 725 pulmonary fibrosis cases and 450 normal cases.

Main Results:

  • Achieved high performance metrics: 98% accuracy, 99% precision, and 99% sensitivity.
  • Demonstrated the model's effectiveness in predicting pulmonary fibrosis risk in COVID-19 patients.
  • The model successfully differentiated between patients with and without pulmonary fibrosis.

Conclusions:

  • The proposed XGBoost-based model is the first to integrate EHR and HRCT data for detecting COVID-19-associated pulmonary fibrosis.
  • This approach assists clinicians in identifying severe COVID-19 cases at risk of developing pulmonary fibrosis.
  • Early identification facilitates timely intervention, potentially reducing life-threatening conditions associated with post-COVID-19 lung damage.