Iranian Journal of Psychiatry and Behavioral Sciences

Published by: Kowsar

Comparison of Artificial Neural Network and Logistic Regression Models for Prediction of Psychological Symptom Six Months after Mild Traumatic Brain Injury

Elham Shafiei 1 , Esmaeil Fakharian 1 , * , Abdollah Omidi 2 , Hossein Akbari 3 , Ali Delpisheh 4 and Arash Nademi 5
Authors Information
1 Trauma Research Center, Kashan University of Medical Sciences, Kashan, IR Iran
2 Department of Clinical Psychology, Kashan University of Medical Sciences, Kashan, IR Iran
3 Department of Epidemiology and Biostatistics, School of Public Health, Kashan University of Medical Sciences, Kashan, Iran
4 Prevention of Psychosocial Injuries, Research Centre, Ilam University of Medical Sciences, Ilam, Iran
5 Department of Statistics, Ilam Branch, Islamic Azad University, Ilam, Iran
Article information
  • Iranian Journal of Psychiatry and Behavioral Sciences: September 2017, 11 (3); e5849
  • Published Online: November 5, 2016
  • Article Type: Original Article
  • Received: March 3, 2016
  • Revised: June 28, 2016
  • Accepted: October 28, 2016
  • DOI: 10.17795/ijpbs-5849

To Cite: Shafiei E, Fakharian E, Omidi A, Akbari H, Delpisheh A, et al. Comparison of Artificial Neural Network and Logistic Regression Models for Prediction of Psychological Symptom Six Months after Mild Traumatic Brain Injury, Iran J Psychiatry Behav Sci. 2017 ; 11(3):e5849. doi: 10.17795/ijpbs-5849.

Copyright © 2016, Mazandaran University of Medical Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
1. Background
2. Objectives
3. Materials and Methods
4. Results
5. Discussion
  • 1. Bashiri M, Farshbaf Geranmayeh A. Tuning the parameters of an artificial neural network using central composite design and genetic algorithm. Scientia Iranica. 2011; 18(6): 1600-8[DOI]
  • 2. Wyatt JCAD. Prognostic models: clinically useful or quickly forgotten? Brit M J. 1995; 311: 1539-41[DOI]
  • 3. Buchner A, May M, Burger M, Bolenz C, Herrmann E, Fritsche HM, et al. Prediction of outcome in patients with urothelial carcinoma of the bladder following radical cystectomy using artificial neural networks. Eur J Surg Oncol. 2013; 39(4): 372-9[DOI][PubMed]
  • 4. Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal. 2000; 22(5): 717-27[PubMed]
  • 5. Aladag CH. A new architecture selection method based on tabu search for artificial neural networks. Expert Systems with Applications. 2011; 38(4): 3287-93[DOI]
  • 6. Lv DJ, Zhang Y, Wang XY, Guo XM, Wang CY. [Application of artificial neural network to diagnosis of prostate cancer]. Beijing Da Xue Xue Bao. 2009; 41(4): 469-73[PubMed]
  • 7. Amiri Z, Mohammad K, Mahmoudi M, Parsaeian M, Zeraati H. Assessing the effect of quantitative and qualitative predictors on gastric cancer individuals survival using hierarchical artificial neural network models. Iran Red Crescent Med J. 2013; 15(1): 42-8[DOI][PubMed]
  • 8. Andersson B, Andersson R, Ohlsson M, Nilsson J. Prediction of severe acute pancreatitis at admission to hospital using artificial neural networks. Pancreatology. 2011; 11(3): 328-35[DOI][PubMed]
  • 9. Adoko AC, Yu-Yong J, Li W, Hao W, Zi-Hao W. Predicting tunnel convergence using Multivariate Adaptive Regression Spline and Artificial Neural Network. Tunnelling and Underground Space Technol. 2013; 38: 368-76[DOI]
  • 10. Lin MR, Chiu WT, Chen YJ, Yu WY, Huang SJ, Tsai MD. Longitudinal changes in the health-related quality of life during the first year after traumatic brain injury. Arch Phys Med Rehabil. 2010; 91(3): 474-80[DOI][PubMed]
  • 11. De Silva MJ, Roberts I, Perel P, Edwards P, Kenward MG, Fernandes J, et al. Patient outcome after traumatic brain injury in high-, middle- and low-income countries: analysis of data on 8927 patients in 46 countries. Int J Epidemiol. 2009; 38(2): 452-8[DOI][PubMed]
  • 12. Garber BG, Rusu C, Zamorski MA. Deployment-related mild traumatic brain injury, mental health problems, and post-concussive symptoms in Canadian Armed Forces personnel. BMC Psychiatry. 2014; 14: 325[DOI][PubMed]
  • 13. Draper K, Ponsford J, Schonberger M. Psychosocial and emotional outcomes 10 years following traumatic brain injury. J Head Trauma Rehabil. 2007; 22(5): 278-87[DOI][PubMed]
  • 14. Hessel ASJ, Geyer M, Brahler E. Symptom-Checkliste SCL-90-R: Testth - eoretische Überprüfung und Normierung an einer bevölkerungsrepräsentativen Stichprobe. Diagnostica. 2001; 47: 27-39[DOI]
  • 15. Fakharian E, Omidi A, Shafiei E, Nademi A. Mental health status of patients with mild traumatic brain injury admitted to shahid beheshti hospital of kashan, iran. Arch Trauma Res. 2015; 4(1): 17629[DOI][PubMed]
  • 16. Mohammadkhani P. Psychometric properties of the Brief Symptom Inventory in a sample of recovered Iranian depressed patients. Int J Clin Health Psychol. 2010; 10(3): 541-51
  • 17. Vassallo JL, Proctor-Weber Z, Lebowitz BK, Curtiss G, Vanderploeg RD. Psychiatric risk factors for traumatic brain injury. Brain Inj. 2007; 21(6): 567-73[DOI][PubMed]
  • 18. Chong SL, Liu N, Barbier S, Ong ME. Predictive modeling in pediatric traumatic brain injury using machine learning. BMC Med Res Methodol. 2015; 15: 22[DOI][PubMed]
  • 19. Booth-Kewley S, Schmied EA, Highfill-McRoy RM, Larson GE, Garland CF, Ziajko LA. Predictors of psychiatric disorders in combat veterans. BMC Psychiatry. 2013; 13: 130[DOI][PubMed]
  • 20. Perron BE, Howard MO. Prevalence and correlates of traumatic brain injury among delinquent youths. Crim Behav Ment Health. 2008; 18(4): 243-55[DOI][PubMed]
  • 21. Ahmadizar F, Soltanianb K, AkhlaghianTabb F, Tsoulos I. Artificial neural network development by means of a novel combination of grammatical evolution and genetic algorithm. Engineering Applications of Artificial Intelligence. 2015; 39: 1-13[DOI]
  • 22. Zhou Q, Kwong J, Chen J, Qin W, Chen J, Dong L, et al. Use of artificial neural network to predict warfarin individualized dosage regime in Chinese patients receiving low-intensity anticoagulation after heart valve replacement. Int J Cardiol. 2014; 176(3): 1462-4[DOI][PubMed]
  • 23. Song JH, Venkatesh SS, Conant EA, Arger PH, Sehgal CM. Comparative analysis of logistic regression and artificial neural network for computer-aided diagnosis of breast masses. Acad Radiol. 2005; 12(4): 487-95[DOI][PubMed]
  • 24. Chokmani K. Comparison of ice-affected streamflow estimates computed using artificial neural networks and multiple regression techniques. J Hydrol. 2008; 349(3-4): 383-96[DOI]
  • 25. Seixas JM, Faria J, Souza Filho JB, Vieira AF, Kritski A, Trajman A. Artificial neural network models to support the diagnosis of pleural tuberculosis in adult patients. Int J Tuberc Lung Dis. 2013; 17(5): 682-6[DOI][PubMed]
  • 26. Shi HY, Lee KT, Lee HH, Ho WH, Sun DP, Wang JJ, et al. Comparison of artificial neural network and logistic regression models for predicting in-hospital mortality after primary liver cancer surgery. PLoS One. 2012; 7(4): 35781[DOI][PubMed]
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