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Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry

Ioannidis, Konstantinos, Chamberlain, Samuel R., Treder, Matthias, Kiraly, Franz, Leppink, Eric W., Redden, Sarah A., Stein, Dan J., Lochner, Christine and Grant, Jon E. 2016. Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry. Journal of Psychiatric Research 83 , pp. 94-102. 10.1016/j.jpsychires.2016.08.010

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Abstract

Problematic internet use is common, functionally impairing, and in need of further study. Its relationship with obsessive-compulsive and impulsive disorders is unclear. Our objective was to evaluate whether problematic internet use can be predicted from recognised forms of impulsive and compulsive traits and symptomatology. We recruited volunteers aged 18 and older using media advertisements at two sites (Chicago USA, and Stellenbosch, South Africa) to complete an extensive online survey. State-of-the-art out-of-sample evaluation of machine learning predictive models was used, which included Logistic Regression, Random Forests and Naïve Bayes. Problematic internet use was identified using the Internet Addiction Test (IAT). 2006 complete cases were analysed, of whom 181 (9.0%) had moderate/severe problematic internet use. Using Logistic Regression and Naïve Bayes we produced a classification prediction with a receiver operating characteristic area under the curve (ROC-AUC) of 0.83 (SD 0.03) whereas using a Random Forests algorithm the prediction ROC-AUC was 0.84 (SD 0.03) [all three models superior to baseline models p < 0.0001]. The models showed robust transfer between the study sites in all validation sets [p < 0.0001]. Prediction of problematic internet use was possible using specific measures of impulsivity and compulsivity in a population of volunteers. Moreover, this study offers proof-of-concept in support of using machine learning in psychiatry to demonstrate replicability of results across geographically and culturally distinct settings.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Elsevier
ISSN: 0022-3956
Date of First Compliant Deposit: 10 October 2018
Date of Acceptance: 11 August 2016
Last Modified: 19 Jul 2019 08:05
URI: http://orca.cf.ac.uk/id/eprint/108243

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