An artificial neural network comparison with logistic regression in predicting post-traumatic mental disorders in mild brain injury patients

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

SHAFIEI ELHAM; FAKHARIAN ESMAEIL; OMIDI ABDOLLAH; NADEMI ARASH

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Behavior in Clinical Psychology Year:2018 | Volume:12 | Issue:46 Page(s): 37-46

Download Full-Text

Persian Verion

View:

556

Download:

Cites:

Information Journal Paper

Title

An artificial neural network comparison with logistic regression in predicting post-traumatic mental disorders in mild brain injury patients

Author(s)

SHAFIEI ELHAM | FAKHARIAN ESMAEIL | OMIDI ABDOLLAH | NADEMI ARASH | Issue Writer Certificate

Keywords

artificial neural networkQ1

logistic regressionQ1

post-traumatic mental disordersQ1

Abstract

Although severe brain injury can make people susceptible to mental disorders, there is still debate about traumatic brain injury. The purpose of this study was to compare the power of artificial neural network in predicting post-traumatic mental disorder in mild brain injury patients and logistic regression. For this purpose, in a prospective cohort study, 100 trauma patients referred to the trauma center of Shahid Beheshti Hospital of Kashan during 6 months were compared with 100 healthy people. For modeling, the data were randomly divided into two educational groups (100) and experimental (100 people). The Rock's curve and classification accuracy were used to estimate the predictive power of mental disorder. The results showed that there is a significant difference between the two groups of mild traumatic patients and healthy subjects in terms of mental disorders, and artificial neural network models have better efficiency than logistic regression models. This study showed that in order to predict mental disorder, the diagnostic indices of this factor should be considered at the beginning of the traumatic brain injury patients and then, using the artificial neural network model, predict this factor. The necessity of using this technology in demographic screening is useful in treating patients with trauma and preventing possible problems for such patients.

Cites

No record.

References

No record.

Cite

APA: Copy

SHAFIEI, ELHAM, FAKHARIAN, ESMAEIL, OMIDI, ABDOLLAH, & NADEMI, ARASH. (2018). An artificial neural network comparison with logistic regression in predicting post-traumatic mental disorders in mild brain injury patients. JOURNAL OF THOUGHT & BEHAVIOR IN CLINICAL PSYCHOLOGY, 12(46 ), 37-46. SID. https://sid.ir/paper/172281/en

Vancouver: Copy

SHAFIEI ELHAM, FAKHARIAN ESMAEIL, OMIDI ABDOLLAH, NADEMI ARASH. An artificial neural network comparison with logistic regression in predicting post-traumatic mental disorders in mild brain injury patients. JOURNAL OF THOUGHT & BEHAVIOR IN CLINICAL PSYCHOLOGY[Internet]. 2018;12(46 ):37-46. Available from: https://sid.ir/paper/172281/en

IEEE: Copy

ELHAM SHAFIEI, ESMAEIL FAKHARIAN, ABDOLLAH OMIDI, and ARASH NADEMI, “An artificial neural network comparison with logistic regression in predicting post-traumatic mental disorders in mild brain injury patients,” JOURNAL OF THOUGHT & BEHAVIOR IN CLINICAL PSYCHOLOGY, vol. 12, no. 46 , pp. 37–46, 2018, [Online]. Available: https://sid.ir/paper/172281/en

Related Journal Papers