Soheil Hassanipour1, Haleh Ghaem2, Morteza Arab-Zozani3, Mozhgan Seif4, Mohammad Fararouei4, Elham Abdzadeh5, Golnar Sabetian6, Shahram Paydar7. 1. Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran. 2. Research Center for Health Sciences, Institute of Health, Epidemiology Department, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran. Electronic address: ghaemh@sums.ac.ir. 3. Iranian Center of Excellence in Health Management, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran. 4. Department of Epidemiology, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran. 5. Department of Biology, Faculty of Science, University of Guilan, Rasht, Iran. 6. Anesthesiology and Critical Care Research Center, Shiraz University of Medical Sciences, Shiraz, Iran. 7. Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran.
Abstract
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.
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 traumaticpatients 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 traumaticpatients in both the AUC and accuracy rate. Using an ANN to predict the final implications of traumapatients can provide more accurate clinical decisions.