Health Management & Information Science

Health Management & Information Science

An investigation of data mining techniques of the performance of a decision tree algorithm for predicting causes of traumatic brain injuries in Khatamolanbya Hospital in Zahdan city, 2012 to 2013

Document Type : Articles

Authors
Ahmad Raeesi
Saeed Ebrahimi
Leila Irfan Nia
Goli Arji
Moslem Askani
Abstract
Introduction: The aim of this study was to determine the performance of data mining techniques for predicting the causes of traumaticbrain injuries in Khatamolanbya hospital, Zahdan city.Method: In this cross–sectional, the study population included all patients who died of brain injury. Data were collected by the use of aresearcher- made check list, provided under the direct observation of authorities in this area and analyzed by the data mining softwareof Clementine 12.0.Results: According to the results of this algorithm, C5.0 decision tree algorithm has an accuracy of 81.4 percent, the highest precision;then, the algorithm is C & R(The Classification and Regression) with 77.8 percent.Conclusion: Overall, it can be concluded from the decision tree algorithm that age is one of the leading causes of traumatic braininjuries . The results showed that all the cases involving traumatic lesions of the brain lead to the patient’s death.. Although in somealgorithms, some of the variables are important, they cannot be used alone as the main variable to be taken into account for the deathof the patient.Keywords: Data mining, Prediction of the factors of traumatic brain injuries
  1. Theodoraki E-M. Innovative data mining approaches for
  2. outcome prediction of trauma patients. Journal of Biomedical
  3. Science and Engineering. 2010;03(08):791-8.
  4. Meyer AA. Death and disability from injury: a global
  5. challenge. J Trauma. 1998;44(1):1-12.
  6. Wu X, Hu J, Zhuo L, Fu C, Hui G, Wang Y, et al. Epidemiology
  7. of Traumatic Brain Injury in Eastern China, 2004: A
  8. Prospective Large Case Study. The Journal of Trauma: Injury,
  9. Infection, and Critical Care. 2008;64(5):1313-9.
  10. Arji G. A conceptualmodelfordata-miningapproa
  11. chtopredictmyocardialinfarctionin 1390. Tehran:
  12. SchoolofAlliedMedicalSciencesand Health Services; 1390.
  13. Adel A, Ahmadi P, Sebt M. Desining Model for Choosing
  14. human resources with data mining approach].[In Persian].
  15. Journalof Iranian Technology. 2010;2(4):5.
  16. Andelic N, Sigurdardottir S, Brunborg C, Roe C. Incidence
  17. of Hospital-Treated Traumatic Brain Injury in the Oslo
  18. Population. Neuroepidemiology. 2008;30(2):120-8.
  19. Bali RK. Clinical knowledge management: opportunities and
  20. challenges. IGI Global; 2005.
  21. Chen H, Fuller SS, Friedman C, Hersh W. 2005;8.
  22. Ho T. Data Mining and Data Warehousing. Prentice Hall;
  23. 10. LaTour K, Eichenwald S. Health Information
  24. Management: Concepts, Principles, and Practice. AHIMA:
  25. Chicago. 2002:478-80.
  26. Tavakoli N, Jahanbakhsh M. Opportunities and Challenges
  27. of HER Implementation in Isfahan [Project] [3. [In Persian]].
  28. Isfahan: The university of Isfahan: School of Informatics and
  29. Management; 2010.
  30. Wager K, Lee F. Glaser JP.Managing Health Care Information
  31. Systems: A Practical Approach for HealthCare Executives.
  32. New Jersey: John Wiley & Sons; 2005.
  33. Wickramasinghe N, Gupta J, Sharma S. Creating Knowledge-
  34. Based Healthcare Organizations. Hershey: IdeaGroupInc
  35. (IGI); 2005.
  36. Wyatt JC. Keeping up: learning in the workplace. Bmj.
  37. ;331(7525):1129-32.
Health Management & Information Science
Volume 1, Issue 2 - Serial Number 2
5
Spring 2014
Pages 28-30