Prediction of mental disorders after Mild Traumatic Brain Injury: principle component Approach


Arash Nademi 1 , Elham Shafiei 2 , * , Esmaeil Fakharian 3 , Abdollah Omidi 4

1 Department of Statistics, Ilam Branch, Islamic Azad University, Ilam, Iran.

2 Psych osocial Injuries Research Center, Ilam University of Medical Science s , Ilam, I r an.

3 Truma Research Center, Kashan University of Medical Sciences, Kashan, Iran.

4 Department of Clinical Psychology, Kashan University of Medical Sciences, Kashan, Iran.

How to Cite: Nademi A , Shafiei E , Fakharian E , Omidi A . Prediction of mental disorders after Mild Traumatic Brain Injury: principle component Approach, Hormozgan Med J. 2018 ; 22(1):e87331. doi: 10.29252/hmj.22.1.38.


Hormozgan Medical Journal: 22 (1); e87331
Published Online: March 04, 2018
Article Type: Research Article
Received: October 08, 2017
Accepted: March 04, 2018


Introduction: In Processes Modeling, when there is relatively a high correlation between covariates, multicollinearity is created, and it leads to reduction in model's efficiency. In this study, by using principle component analysis, modification of the effect of multicolinearity in Artificial Neural Network (ANN) and Logistic Regression (LR) has been studied. Also, the effect of multicolinearity on the accuracy of prediction of mental disorders after trauma in patients with Mild Traumatic Brain Injury has been investigated.
Methods: In a prospective cohort Study, first, during 6 months period, 100 patients with Mild Traumatic Brain Injury have been selected .Then, by using Primary Covariates and Principle Component Analysis, Logistic Regression and ANN models have been conducted and based on these models prediction have been done. (Receiver Operating Characteristic) ROC curve and Accuracy Rate have been used to compare the strength of model’s prediction.
Results: The results revealed that Accuracy Rate for ANN before and after applying principle component analysis are 84.22 and 91.23% respectively, and for Logistic Regression models are 72.33% and 74.89% respectively.
Conclusion: The study showed that the Accuracy Rate was higher for models based on Principle Component Analysis including primary covariates; hence, when multicolinearity exists, models that use the principle component for prediction of mental disorders are more effective compare to other methods. Also, ANN Models are more effective than Regression models.

© 2018, Hormozgan Medical Journal. 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.



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