Comparison of artificial neural networks and logistic regression for determination of social-cognitive factors impacting drug abstinence. (3rd May 2020)

• Record Type: Journal Article
• Authors: Hajihosseini, Morteza; Norozi, Ensiyeh; Azimi, Tara; Ahmadinia, Hassan
• Is Part Of: Journal of substance use. Volume 25:Number 3(2020)
• Page Start: 295
• Page End: 300
• Publication Date: 2020-05-03
• Subjects: Neural networks -- logistic regression -- abstinence; Substance abuse -- Periodicals; Medication abuse -- Periodicals; 362.2905
• DOI: 10.1080/14659891.2019.1690060
• Languages: English
• ISSNs: 1465-9891

Abstract

ABSTRACT: Background: Logistic regression model (LRM) are popular in public health studies. However, most studies have found that artificial neural networks (ANNs) have better performance than other methods especially when the relationship between the response and independent variables is complex. We aimed to compare the performance of LRM and ANNs in the determination of drug abstinence predictors. Methods: The participants of this prospective study included 317 male addicts recruited through convenience sampling in five short-term residential substance use treatment programs in Southern Khorasan, the eastern province of Iran. Eligibility criteria included being in a detoxification stage and having a primary diagnosis of substance dependency disorder based on the diagnostic criteria of the DSM-IV-TR. The performance of the models was compared using the area under the ROC (AUC). Results: LRM and ANNs method correctly classified 82.2% and 84.4% of the participants as an abstinent group, respectively. The areas under ROC curve were 90 and 91.2, the kappa statistic was 0.643 and 0.688 for LRM and ANN models, respectively. Conclusions: The results showed classification performance was not significantly different. We concluded that the selection of prediction techniques for predicting drug abstinence factors depended on our background knowledge on the data pattern.