Detecting internet addiction disorder using bayesian networks

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.