| Autor: |
Di Z; School of Electronic and Information Engineering, Tongji University, Shanghai, China., Gong X; School of Electronic and Information Engineering, Tongji University, Shanghai, China., Shi J; East Hospital, Tongji University School of Medicine, Shanghai 200120, China., Ahmed HOA; Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, Middlesex, UK., Nandi AK; Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, Middlesex, UK.; School of Electronic and Information Engineering, Tongji University, Shanghai, China. |
| Jazyk: |
angličtina |
| Zdroj: |
Addictive behaviors reports [Addict Behav Rep] 2019 Jul 11; Vol. 10, pp. 100200. Date of Electronic Publication: 2019 Jul 11 (Print Publication: 2019). |
| DOI: |
10.1016/j.abrep.2019.100200 |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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