Comparison of artificial neural network and logistic regression models for prediction of outcomes in trauma patients: A systematic review and meta-analysis

Injury. 2019 Feb;50(2):244-250. doi: 10.1016/j.injury.2019.01.007. Epub 2019 Jan 11.


Background: Currently, two models of artificial neural network (ANN) and logistic regression (LR) are known as models that extensively used in medical sciences. The aim of this study was to compare the ANN and LR models in prediction of Health-related outcomes in traumatic patients using a systematic review.

Methods: The study was planned and conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist. A literature search of published studies was conducted using PubMed, Embase, Web of knowledge, Scopus, and Google Scholar in May 2018. Joanna Briggs Institute (JBI) checklists was used for assessing the quality of the included articles.

Results: The literature searches yielded 326 potentially relevant studies from the primary searches. Overall, the review included 10 unique studies. The results of this study showed that the area under curve (AUC) for the ANN was 0.91, (95% CI 0.89-0.83) and 0.89, (95% CI 0.87-90) for the LR in random effect model. The accuracy rate for ANN and LR in random effect models were 90.5, (95% CI, 87.6-94.2) and 83.2, (95% CI 75.1-91.2), respectively.

Conclusion: The results of our study showed that ANN has better performance than LR in predicting the terminal outcomes of traumatic patients in both the AUC and accuracy rate. Using an ANN to predict the final implications of trauma patients can provide more accurate clinical decisions.

Keywords: Artificial neural network; Logistic regression; Systematic review; Trauma.

Publication types

  • Comparative Study
  • Systematic Review

MeSH terms

  • Area Under Curve
  • Hospital Mortality
  • Humans
  • Injury Severity Score
  • Logistic Models*
  • Neural Networks, Computer*
  • Predictive Value of Tests
  • Survival Analysis
  • Wounds and Injuries / classification
  • Wounds and Injuries / mortality*