Machine learning-based evaluation of prognostic factors for mortality and relapse in patients with acute lymphoblastic leukemia: a comparative simulation study.

Journal: BMC medical informatics and decision making
PMID: 39285373

Abstract

BACKGROUND: Predicting mortality and relapse in children with acute lymphoblastic leukemia (ALL) is crucial for effective treatment and follow-up management. ALL is a common and deadly childhood cancer that often relapses after remission. In this study, we aimed to apply and evaluate machine learning-based models for predicting mortality and relapse in pediatric ALL patients.

Authors

  • Zahra Mehrbakhsh
    Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
  • Roghayyeh Hassanzadeh
    Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
  • Nasser Behnampour
    Department of Biostatistics and Epidemiology, School of Health, Golestan University of Medical Sciences, Gorgan, Iran.
  • Leili Tapak
    Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
  • Ziba Zarrin
    Department of Photogrammetry and Remote Sensing, K. N. Toosi University of Technology, Tehran, Iran.
  • Salman Khazaei
    Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran.
  • Irina Dinu
    School of Public Health, University of Alberta, Edmonton, Canada.

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