J Psychiatr Res. 2016 Aug 15;83:94-102. doi: 10.1016/j.jpsychires.2016.08.010.
Ioannidis K1, Chamberlain SR1, Treder MS2, Kiraly F3, Leppink EW4, Redden SA4, Stein DJ5, Lochner C5, Grant JE6.
Author information
- 1Department of Psychiatry, University of Cambridge, UK; Cambridge and Peterborough NHS Foundation Trust, Cambridge, UK.
- 2Behavioural and Clinical Neuroscience Institute, University of Cambridge, UK.
- 3University College London, Department of Statistical Science, London, UK.
- 4Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA.
- 5US/UCT MRC Unit on Anxiety & Stress Disorders, Department of Psychiatry, University of Stellenbosch, South Africa.
- 6Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA. Electronic address: [email protected].
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.
KEYWORDS:
ADHD; Compulsivity; Impulsivity; Internet use; Machine learning; OCD
PMID:27580487
DOI:10.1016/j.jpsychires.2016.08.010
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