Machine Learning Classification Model for Identifying Internet Addiction among University Students - ICCTech 2023

Aug 4, 2026

Internet addiction is a growing concern among university students who must use the Internet for study while also engaging with entertainment functions. Traditional measurement based on Young’s Internet Addiction Test relies on questionnaire integrity and participant literacy, which can introduce inconsistencies. A machine learning approach is developed to evaluate Internet addiction using EEG low alpha-band data converted into spectrograms. Three models—CNN, KNN, and logistic regression—are trained and compared, with CNN achieving the best overall performance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages5

Reading Time13 min

AccessFree to download

Introduction

Background and prevalence of internet addiction among university students

Limitations of questionnaire-based assessment

Motivation for EEG-based identification

2023 2nd International Conference on Computer Technologies (ICCTech) | 978-1-6654-5582-4/23/$31.00 ©2023 IEEE | DOI: 10. 1 109/ ICCTECH57499. 2023.00010 2023 2nd International Conference on Computer Technologies (ICCTech) Machine Learning Classification Model for Identifying Internet Addiction among University Students Tan Hui Theng, Mun Hou Kit Department of Mechatronic and Biomedical Engineering, Faculty of Engineering and Science Universiti Tunku Abdul Rahman, Bandar Sungai Long, Selangor, Malaysia [e-mail: mtht.0920@gmail.com](e-mail: mtht.0920@gmail.com), [munhk@utar.my](munhk@utar.my) Dini Handayani Computer Science Department, Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Selangor, Malaysia e-mail: [dinihandayani@iium.edu.my](dinihandayani@iium.edu.my) Abstract—In this era of globalization, Internet addiction is a concerning issue, especially among university students as they are required to use the internet for academic purposes. However, things might go wrong when they are addicted to the Internet as the Internet does not only provide knowledge but also entertainment such as music, videos, games, social media, etc. Internet addiction was exposed to the public when Young introduced Internet addiction in her study as well as an assessment for Internet addiction known as Young’s Internet addiction test (IAT) which is a questionnaire. Nonetheless, there are some issues associated with the questionnaire regarding the integrity and literacy of the participants as well as the experience of the specialist which might introduce inconsistencies in the assessment of one’s Internet addiction level. Hence, the machine learning algorithm is introduced to replace the conventional assessment method for Internet addiction. In this study, three machine learning models are developed and compared. The three models include convolutional neural network (CNN), K-nearest neighbours (KNN), and logistic regression (LR). The low Alpha power band of the EEG data is transformed into spectrograms and utilized as the input for the machine learning models. The spectrograms are presented as images and fed into the CNN model. On the other hand, as KNN and LR could not take in images as the input data, the magnitude of each frequency in every time segment of each spectrogram is computed and fed into the KNN and LR. The results show that CNN gives the best performance in terms of overall accuracy, precision, recall, and F1-score, while KNN gives the most consistent performance. Keywords- Machine Learning, Internet addiction, EEG, CNN, KNN, LR I. INTRODUCTION Owing to the emergence of the Internet and the advanced development of digital technology, the use of the Internet has become prevalent in communities around the world, the Internet is undoubtedly beneficial to human beings in terms of convenience and living quality by easing and simplifying information collection as well as providing entertainment anytime and anywhere. Unfortunately, pros are always accompanied by cons. The widespread Internet access and the advantages provided by the Internet have led to a sort of addiction among Internet users worldwide, which is most commonly known as ‘Internet addiction’. Other terms such as ‘problematic computer use’, ‘compulsive computer use’,‘pathological Internet use’, and ‘internetomania’ also describe this condition [1] . In this globalization era, it is not surprising that the majority or even all university students worldwide have Internet access and knowledge to use the Internet and digital devices that allow access to the Internet. Many of them are even taught about the usage of digital devices and the Internet. This raised an issue concerning Internet addiction (IA) among university students, which attracts many researchers around the world to study. For instance, the prevalence of IA among university students was 85% at Wollo University, Ethiopia in 2019 [2]; 87.7% at Tant

FAQs

Why does the document propose machine learning instead of using Young’s Internet Addiction Test?

The questionnaire depends on participant integrity and literacy, and it lacks mechanisms to detect fake responses. These factors can produce inconsistent Internet addiction assessments.

How is EEG data used in the proposed classification models?

The study transforms low alpha-band EEG signals into spectrograms. CNN uses spectrogram images directly, while KNN and logistic regression use computed frequency magnitudes from each time segment.

Which model performs best and how do the results compare across models?

CNN provides the highest overall accuracy, precision, recall, and F1-score. KNN shows the most consistent performance among the compared models.

Aug 4, 2026

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