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Updated: Oct 5, 2025

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Performance analysis of data mining algorithms for diagnosing COVID-19.

Raoof Nopour1, Hadi Kazemi-Arpanahi2,3, Mostafa Shanbehzadeh4

  • 1Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.

Journal of Education and Health Promotion
|January 24, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict COVID-19 early. The J-48 algorithm demonstrated superior performance in diagnosing COVID-19 cases, offering a reliable computational tool.

Keywords:
Artificial intelligenceCOVID-19coronavirusdata miningdiagnosismachine learning

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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Machine Learning in Healthcare

Background:

  • The global COVID-19 pandemic necessitates early detection systems.
  • Accurate prediction of COVID-19 is critical for disease containment.
  • Machine learning offers potential for developing effective diagnostic models.

Purpose of the Study:

  • To identify the most effective machine learning models for COVID-19 prediction.
  • To evaluate and compare the performance of various data mining algorithms for COVID-19 diagnosis.
  • To establish a reliable computational model for early COVID-19 detection.

Main Methods:

  • Utilized data from 435 suspected COVID-19 cases.
  • Applied Chi-square method to identify key diagnostic features (21 variables).
  • Evaluated eight data mining algorithms: MLP, J-48, Bayes Net, logistic regression, K-star, random forest, Ada-boost, and SMO.

Main Results:

  • The J-48 algorithm exhibited the highest performance.
  • J-48 achieved a true-positive rate of 0.85 and an F-score of 0.85.
  • Area under the ROC curve for J-48 was 0.68, indicating good discriminative ability.

Conclusions:

  • The J-48 algorithm is a suitable computational model for diagnosing COVID-19.
  • Early detection through machine learning can aid in managing the COVID-19 pandemic.
  • The study highlights the potential of J-48 in clinical decision support for infectious diseases.