Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm.

Journal: European journal of internal medicine
PMID: 38458880

Abstract

It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA) based on 15 easily accessible hematological indices. A database of 1596 patients with COVID-19 was used; it was divided into 1257 training datasets (80 % of the database) for training the algorithms and 339 testing datasets (20 % of the database) to check the reliability of the algorithms. The optimal combination of hematological indicators that gives the best prediction consists of only four hematological indicators as follows: neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase, ferritin, and albumin. The best prediction corresponds to a particularly high accuracy of 97.12 %. In conclusion, our novel approach provides a robust model based only on basic hematological parameters for predicting the risk for ICU admission and optimize COVID-19 patient management in the clinical practice.

Authors

  • Panagiotis G Asteris
    Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, 14121 Athens, Greece.
  • Amir H Gandomi
    Faculty of Engineering Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia.
  • Danial J Armaghani
    Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.
  • Styliani Kokoris
    Laboratory of Hematology and Hospital Blood Transfusion Department, University General Hospital 'Attikon', NKUA, Medical School, Athens, Greece.
  • Anastasia T Papandreadi
    Software and Applications Department, University General Hospital "Attikon", National and Kapodistrian University of Athens, Medical School, Greece.
  • Anna Roumelioti
    Department of Hematology and Lymphoma BMTU, Evangelismos General Hospital, Athens, Greece.
  • Stefanos Papanikolaou
    NOMATEN Centre of Excellence, National Center for Nuclear Research, ulica A. Sołtana 7, 05-400 Swierk/Otwock, Poland.
  • Markos Z Tsoukalas
    Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece.
  • Leonidas Triantafyllidis
    Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece.
  • Evangelos I Koutras
    Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece.
  • Abidhan Bardhan
    Civil Engineering Department, National Institute of Technology Patna, Patna, India.
  • Ahmed Salih Mohammed
    Engineering Department, American University of Iraq, Sulaimani, Kurdistan-Region, Iraq.
  • Hosein Naderpour
    Institute of Industrial Science, University of Tokyo, Tokyo, Japan.
  • Satish Paudel
    Department of Civil and Environmental Engineering, University of Nevada, Reno, US.
  • Pijush Samui
    Department of Civil Engineering, National Institute of Technology Patna, India.
  • Ioannis Ntanasis-Stathopoulos
    Department of Clinical Therapeutics, Medical School, Faculty of Medicine, National Kapodistrian University of Athens, Athens, Greece.
  • Meletios A Dimopoulos
    Department of Clinical Therapeutics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece.
  • Evangelos Terpos
    Department of Clinical Therapeutics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece.

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