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A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data.

Md Abdul Awal1, Mehedi Masud2, Md Shahadat Hossain3

  • 1Electronics and Communication Engineering DisciplineKhulna University Khulna 9208 Bangladesh.

IEEE Access : Practical Innovations, Open Solutions
|November 17, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning framework for rapid COVID-19 patient identification using inpatient data. The optimized model, eXtreme Gradient Boosting, achieved a 97% Kappa index, offering a faster, cost-effective solution for pandemic control.

Keywords:
ADASYNBayesian optimizationCOVID-19classificationinpatient's facility data

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Area of Science:

  • Medical Informatics
  • Machine Learning
  • Epidemiology

Background:

  • The COVID-19 pandemic necessitates rapid and accurate patient identification to curb transmission.
  • Current diagnostic methods can be time-consuming, delaying critical interventions.

Purpose of the Study:

  • To develop and optimize a machine learning framework for efficient COVID-19 detection using inpatient data.
  • To provide a user-friendly, cost-effective, and time-efficient solution for pandemic management.

Main Methods:

  • Utilized Bayesian optimization for hyperparameter tuning and the ADAptive SYNthetic (ADASYN) algorithm for class balancing.
  • Applied the framework to nine state-of-the-art classifiers, with a focus on eXtreme Gradient Boosting (XGB).
  • Employed SHapely Adaptive exPlanations (SHAP) for feature importance analysis.

Main Results:

  • The eXtreme Gradient Boosting classifier achieved the highest Kappa index of 97.00%.
  • The proposed approach, incorporating ADASYN, improved the Kappa index by 96.94% compared to methods without it.
  • Bayesian optimization demonstrated superior efficiency over grid search and random search.

Conclusions:

  • The developed machine learning framework enables faster and more accurate COVID-19 patient tracing than conventional methods.
  • The study highlights the potential of XGBoost and ADASYN for improving diagnostic efficiency in pandemics.
  • Demonstrated applications include a clinically operable decision tree and a decision support system for healthcare professionals.