Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms.

Journal: BMC medical research methodology
Published Date:

Abstract

BACKGROUND: Trauma is one of the most critical public health issues worldwide, leading to death and disability and influencing all age groups. Therefore, there is great interest in models for predicting mortality in trauma patients admitted to the ICU. The main objective of the present study is to develop and evaluate SMOTE-based machine-learning tools for predicting hospital mortality in trauma patients with imbalanced data.

Authors

  • Roghayyeh Hassanzadeh
    Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
  • Maryam Farhadian
  • Hassan Rafieemehr
    Department of Medical Laboratory Sciences, School of Paramedicine, Hamadan University of Medical Sciences, Hamadan, Iran. ha.rafee@umsha.ac.ir.