Abstract
21st century being the digital age has produced social, economic and communication revolution. Proliferation of Internet has made a globally connected world. Internet per se is a harmless technology with significant benefits, on contrary its excessive usage and dependence leads to high risk of addiction. It is preordained society need and requirement of continual research to develop efficient tools to identify and predict the potential Internet Addiction Disorder among the internet users. Our aim is to automate task of predicting prevalence of Internet Addiction disorder using Bayesian Network and propose a machine learning graphical framework, namely, Internet Addiction Disorder Bayesian Network. In this work, we exploit the unique features of Bayesian Network to explore the influence of causal symptoms on the probability of occurrence of Internet Addiction Disorder (IAD). The model is constructed with Internet Addiction Test as platform and Internet Addiction Disorder absence or presence is measured through six parameters of Internet Addiction Test (Salience, Excessive use, Neglect work, Anticipation, Lack of control, Neglect Social Life). The six attributes are classified into four groups: normal, mild, moderate and severe on the basis of the item scores obtained by the individuals in the parameters, which are summed to obtain the total score. The total score is utilized to classify the samples into two groups: IAD Present and IAD Absent. To achieve graphical user interface for the model, a high performance Netica software is used for the study. The results obtained are promising and reveals that the model can predict the IAD presence and absence with 100% accuracy. The model also shows that out of six parameters, excessive use of internet plays significant role in increasing the risk of IAD preceded by Salience.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Brent Conrad Techaddiction Internet Addiction – Symptoms, Signs, Treatment, and FAQS. http://www.techaddiction.ca/internet-addiction.html
Brandongaille statistics 33 Interesting Internet Addiction Statistics (2017). http://brandongaille.com/32-interesting-internet-addiction-statistics/
Young, K.: IAT Manual – Center for Internet Addiction (1998). https://www.netaddiction.com/wp-content/uploads/2015/11/IAT-Manual.doc
Flisher, C.: Getting plugged in: an overview of Internet addiction. J. Paediatr. Child Health 46, 557–559 (2010)
American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders. (4th edition, Text Review). American Psychiatric Association, Washington D.C. (2000)
Cash, H., Rae, C.D., Steel, A.H., Winkler, A.: Internet addiction: A brief summary of research and practice. Current psychiatry reviews 8(4), 292–298 (2012)
Gregory, C.: PSYCOM Internet addiction disorder Signs, Symptoms, and Treatments (2017). https://www.psycom.net/iadcriteria.html
Ioannidis, K., Chamberlain, S.R., Treder, M.S., Kiraly, F., Leppink, E.W., Redden, S.A., Stein, D.J., Lochner, C., Grant, J.E.: Problematic internet use(PIU): associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry. J. Psychiatr. Res. 83, 94–102 (2016)
Nandhini, C., Krishnaveni, K.: Evaluation of internet addiction disorder among students. Indian J. Sci. Technol. 9(19) (2016). https://doi.org/10.17485/ijst/2016/v9i19/93864
Bishop, C.M.: Pattern Recognition and Machine Learning. LLC, pp. 14–15, 359–360 Springer Science+Business Media, Berlin (2006)
Ben-Gal, I.: Bayesian networks. In: Ruggeri, F., Faltin, F., Kenett, R. (eds.) Encyclopedia of Statistics in Quality and Reliability. Wiley, Hoboken (2007)
Yu, H., Chen, G., Liu, D.: A simplified Bayesian network model applied in crop or animal disease diagnosis. In: Li, D., Zhao, C. (eds.) CCTA 2008. IAICT, vol. 294, pp. 1001–1009. Springer, Boston, MA (2009). https://doi.org/10.1007/978-1-4419-0211-5_25
Application for belief networks and influence diagrams. Norsys Software Corporation. http://www.norsys.com
Lucas, P.J., Van der Gaag, L.C., Abu-Hanna, A.: Bayesian networks in biomedicine and health-care. Artif. Intelli. Med. 30(3), 201–214 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Singh, A., Babbar, S. (2018). Detecting Internet Addiction Disorder Using Bayesian Networks. In: Panda, B., Sharma, S., Roy, N. (eds) Data Science and Analytics. REDSET 2017. Communications in Computer and Information Science, vol 799. Springer, Singapore. https://doi.org/10.1007/978-981-10-8527-7_8
Download citation
DOI: https://doi.org/10.1007/978-981-10-8527-7_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8526-0
Online ISBN: 978-981-10-8527-7
eBook Packages: Computer ScienceComputer Science (R0)