title: Identifying Important Attributes for Prognostic Prediction in Traumatic Brain Injury Patients. A Hybrid Method of Decision Tree and Neural Network.
abstract: BACKGROUND
  Generally, traumatic brain injury (TBI) patients do not have a stable condition, particularly after the first week of TBI. Hence, indicating the attributes in prognosis through a prediction model is of utmost importance since it helps caregivers with treatment-decision options, or prepares the relatives for the most-likely outcome.
  
  
  OBJECTIVE
  This study attempted to determine and order the attributes in prognostic prediction in TBI patients, based on early clinical findings. A hybrid method was employed, which combines a decision tree (DT) and an artificial neural network (ANN) in order to improve the modeling process.
  
  
  METHODS
  The DT approach was applied as the initial analysis of the network architecture to increase accuracy in prediction. Afterwards, the ANN structure was mapped from the initial DT based on a part of the data. Subsequently, the designed network was trained and validated by the remaining data. 5-fold cross-validation method was applied to train the network. The area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy rate were utilized as performance measures. The important attributes were then determined from the trained network using two methods: change of mean squared error (MSE), and sensitivity analysis (SA).
  
  
  RESULTS
  The hybrid method offered better results compared to the DT method. The accuracy rate of 86.3 % vs. 82.2 %, sensitivity value of 55.1 % vs. 47.6 %, specificity value of 93.6 % vs. 91.1 %, and the area under the ROC curve of 0.705 vs. 0.695 were achieved for the hybrid method and DT, respectively. However, the attributes' order by DT method was more consistent with the clinical literature.
  
  
  CONCLUSIONS
  The combination of different modeling methods can enhance their performance. However, it may create some complexities in computations and interpretations. The outcome of the present study could deliver some useful hints in prognostic prediction on the basis of early clinical findings for TBI patients.
authors: Saeedeh Pourahmad, Colorectal Research Center and Biostatistics Department, School of Medicine, Shiraz University of Medical Sciences, 71345 -1874, Shiraz, Iran, E-mail: pourahmad@sums.ac.ir.
authors: Saeedeh
authors: S
authors: Pourahmad
authors: Iman
authors: I
authors: Hafizi-Rastani
authors: Hosseinali
authors: H
authors: Khalili
authors: Shahram
authors: S
authors: Paydar
mesh_terms: D000328:Adult; D000368:Aged; D000070642:Brain Injuries, Traumatic / Q000175:diagnosis*; D003663:Decision Trees; D023261:Glasgow Outcome Scale; D006801:Humans; D008875:Middle Aged; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011379:Prognosis
keywords: Prognostic prediction; artificial neural network; decision tree; hybrid method; traumatic brain injury patients