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Machine Learning Models for Predicting Corticosteroid Therapy Necessity in COVID-19 Patients: A Comparative Study

Author

Listed:
  • Mujiba Shaima

    (Department of Computer Science, Monroe College, New Rochelle, New York, USA)

  • Norun Nabi

    (Department of Information Technology, Washington University of Science and Technology, Alexandria, Virginia, USA)

  • Md Nasir Uddin Rana

    (Department of Computer Science, Monroe College, New Rochelle, New York, USA)

  • Ahmed Ali Linkon

    (Department of Computer Science, Westcliff University, Irvine, California)

  • Badruddowza

    (Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA)

  • Md Shohail Uddin Sarker

    (Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA)

  • Nishat Anjum

    (Department of Computer Science, University of South Dakota, Vermillion, South Dakota, USA)

  • Hammed Esa

    (Department of Business Administration, International American University Los Angeles, California, USA)

Abstract

This study analyzes machine learning algorithms to predict the need for corticosteroid (CS) therapy in COVID-19 patients based on initial assessments. Using data from 1861 COVID-19 patients, parameters like blood tests and pulmonary function tests were examined. Decision Tree and XGBoost emerged as top performers, achieving accuracy rates of 80.68% and 83.44% respectively. Multilayer Perceptron and AdaBoost also showed competitive performance. These findings highlight the potential of AI in guiding CS therapy decisions, with Decision Tree and XGBoost standing out as effective tools for patient identification. This research offers valuable insights for personalized medicine in infectious disease management.

Suggested Citation

  • Mujiba Shaima & Norun Nabi & Md Nasir Uddin Rana & Ahmed Ali Linkon & Badruddowza & Md Shohail Uddin Sarker & Nishat Anjum & Hammed Esa, 2024. "Machine Learning Models for Predicting Corticosteroid Therapy Necessity in COVID-19 Patients: A Comparative Study," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 6(1), pages 217-224, January.
    • Handle: RePEc:alk:jcsts0:v:6:y:2024:i:1:id:6946
    DOI: 10.32996/jcsts.2024.6.1.25
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