USE OF ARTIFICIAL NEURAL NETWORK VERSUS LOGISTIC REGRESSION TO PREDICT POST-TRAUMATIC MENTAL DISORDERS

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 E.; FAKHARIAN E.; OMIDI A.; AKBARI H.; DELPISHEH A.; Nademi A.

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

Journal Paper

Paper Information

Journal: JOURNAL OF ILAM UNIVERSITY OF MEDICAL SCIENCES Year:2016 | Volume:24 | Issue:4 Page(s): 11-20

Download Full-Text

Persian Verion

View:

961

Download:

Cites:

Information Journal Paper

Title

USE OF ARTIFICIAL NEURAL NETWORK VERSUS LOGISTIC REGRESSION TO PREDICT POST-TRAUMATIC MENTAL DISORDERS

Author(s)

Keywords

ANTICIPATIONQ1

MENTAL ILLNESSQ2

ARTIFICIAL NEURAL NETWORKQ1

LOGISTIC REGRESSIONQ1

MILDTRAUMATIC BRAIN INJURYQ1

Abstract

Introduction: Nowadays, the ARTIFICIAL NEURAL NETWORKs have received much attention in predicting the effects of multiple variables and complex relationships in aparticular variables. In this study, we have focused on the use of ARTIFICIAL NEURAL NETWORK versus logisticre gression to predict post-traumatic mental disorders.Materials & methods: In a prospective cohort study, we covered 100 traumapatients admitted to the trauma center of Shahid Beheshti Hospital of Kashan duringa six month period. The patients were then randomly divided into two training (n=50)and experimental (n=50) groups.14variablesincluding age, sex, occupation, education level, marital status, socioeconomic status, history of mentalil lnessin the immediate family, history of being hospitalized in neurosurgeryunit, historyof trauma, history of underlyingdisease, history of psychological drug use, history of anesthesia, history of alcohol use, and history of substance abuse were totallyinvestigated.300artificial neuralnet work sandlogistic regressions were studied in the first group and then the predicted values were compared in the second group using the two models. The ROC curve and classification accuracy toolwere applied to estimate the predictive power of mental disorder.Findings: The results showed that the accurate index for predicting the disorderwas90.65% for the neural network model, while it was 75.96% for the logisticre gression model.Discussion & conclusions: The ARTIFICIAL NEURAL NETWORK models appeared to be more powerful in predictingmental disorder versus the LOGISTIC REGRESSION model.

Cites

No record.

References

No record.

Cite

APA: Copy

SHAFIEI, E., FAKHARIAN, E., OMIDI, A., AKBARI, H., DELPISHEH, A., & Nademi, A.. (2016). USE OF ARTIFICIAL NEURAL NETWORK VERSUS LOGISTIC REGRESSION TO PREDICT POST-TRAUMATIC MENTAL DISORDERS. JOURNAL OF ILAM UNIVERSITY OF MEDICAL SCIENCES, 24(4), 11-20. SID. https://sid.ir/paper/90162/en

Vancouver: Copy

SHAFIEI E., FAKHARIAN E., OMIDI A., AKBARI H., DELPISHEH A., Nademi A.. USE OF ARTIFICIAL NEURAL NETWORK VERSUS LOGISTIC REGRESSION TO PREDICT POST-TRAUMATIC MENTAL DISORDERS. JOURNAL OF ILAM UNIVERSITY OF MEDICAL SCIENCES[Internet]. 2016;24(4):11-20. Available from: https://sid.ir/paper/90162/en

IEEE: Copy

E. SHAFIEI, E. FAKHARIAN, A. OMIDI, H. AKBARI, A. DELPISHEH, and A. Nademi, “USE OF ARTIFICIAL NEURAL NETWORK VERSUS LOGISTIC REGRESSION TO PREDICT POST-TRAUMATIC MENTAL DISORDERS,” JOURNAL OF ILAM UNIVERSITY OF MEDICAL SCIENCES, vol. 24, no. 4, pp. 11–20, 2016, [Online]. Available: https://sid.ir/paper/90162/en

Related Journal Papers