| 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. |