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

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.

Methods

This retrospective cohort study was conducted on 126 trauma patients admitted to an intensive care unit at Besat hospital in Hamadan Province, western Iran, from March 2020 to March 2021. Data were extracted from the medical information records of patients. According to the imbalanced property of the data, SMOTE techniques, namely SMOTE, Borderli

SUBMITTER: Hassanzadeh R 

PROVIDER: S-EPMC10122327 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature