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Comparative analysis of feature selection techniques for COVID-19 dataset.

Farideh Mohtasham1, MohamadAmin Pourhoseingholi2, Seyed Saeed Hashemi Nazari3

  • 1Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran. f-mohtasham@sbmu.ac.ir.

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Machine learning and feature selection improve COVID-19 mortality prediction. A Hybrid Boruta-VI model with Random Forest accurately identified key risk factors like age and oxygen saturation.

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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Epidemiology

Background:

  • Machine learning (ML) is crucial for early disease detection.
  • Feature selection (FS) algorithms enhance predictive model accuracy by identifying key variables.

Purpose of the Study:

  • To evaluate various FS methods for predicting COVID-19 mortality.
  • To identify critical predictors of adverse outcomes in COVID-19 patients.

Main Methods:

  • Retrospective analysis of 4778 COVID-19 patients.
  • Utilized 115 clinical, laboratory, and demographic features.
  • Compared 13 ML models with filter, embedded, and hybrid FS approaches.

Main Results:

  • Hybrid Boruta-VI with Random Forest achieved superior performance (Accuracy: 0.89, F1: 0.76, AUC: 0.95).
  • Identified age, oxygen saturation, albumin, neutrophils, platelets, and kidney function markers as key predictors.

Conclusions:

  • Advanced FS techniques and ML models can significantly enhance early disease detection.
  • Findings support improved clinical decision-making for COVID-19 patient management.