Detection of IAD based on personality questionnaires of Chinese college students and SVMs

Di, Zonglin; Gong, Xiaoliang; Shi, Jingyu; Ahmed, Hosameldin

doi:10.1109/cisp-bmei.2017.8302282

Cited by 2 publications

(2 citation statements)

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“…Statistical methods can only give the degree of correlation, which cannot be used to predict whether the patients have IA or not directly. This work utilized machine learning using more questionnaires and a relative larger dataset (Di et al, 2017; Gong et al, 2016; Wang et al, 2008) and our results show its efficiency in this kind of task, which provided a new view for the researchers in this area.…”

Section: Discussionmentioning

confidence: 65%

“…The dataset used comes from 580 freshmen from the University and the features used include Self-Control, Attention, Motor, Non-planning, Extraversion, Agreeableness, Conscientiousness, Neuroticism and Openness. Based on the same dataset, (Di, Gong, Shi, Ahmed, & Nandi, 2017) used Support Vector Machines (SVMs) and personality questionnaire data to detect IAD among Chinese college students. The author compared the performance of C-SVM and ν -SVM and did some work to find the influence of sex and age on IAD.…”

Section: Introductionmentioning

confidence: 99%

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Internet addiction disorder detection of Chinese college students using several personality questionnaire data and support vector machine

Gong

Shi

et al. 2019

Addictive Behaviors Reports

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With the unprecedented development of the Internet, it also brings the challenge of Internet Addiction (IA), which is hard to diagnose and cure according to the state-of-art research. In this study, we explored the feasibility of machine learning methods to detect IA. We acquired a dataset consisting of 2397 Chinese college students from the University (Age: 19.17 ± 0.70, Male: 64.17%) who completed Brief Self Control Scale (BSCS), the 11th version of Barratt Impulsiveness Scale (BIS-11), Chinese Big Five Personality Inventory (CBF-PI) and Chen Internet Addiction Scale (CIAS), where CBF-PI includes five sub-features (Openness, Extraversion, Conscientiousness, Agreeableness, and Neuroticism) and BSCS includes three sub-features (Attention, Motor and Non-planning). We applied Student's t -test on the dataset for feature selection and Support Vector Machines (SVMs) including C-SVM and ν -SVM with grid search for the classification and parameters optimization. This work illustrates that SVM is a reliable method for the assessment of IA and questionnaire data analysis. The best detection performance of IA is 96.32% which was obtained by C-SVM in the 6-feature dataset without normalization. Finally, the BIS-11, BSCS, Motor, Neuroticism, Non-planning, and Conscientiousness are shown to be promising features for the detection of IA.

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Section: Discussionmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%