A Comparative Analysis of Contact and Non-Contact Approaches Using Machine Learning for Gaming Disorder Detection

Zhaochen Jiang

2024

Abstract

With the rapid development of the Internet and the gaming industry, video games have become a major form of entertainment, leading to an increase in gaming addiction, which was officially classified as a mental disorder by the World Health Organization in 2018. This article first introduces the concept and current status of gaming disorder and then reviews the application of machine learning (ML) in identifying gaming addiction, analyzing existing research on contact, e.g., Electroencephalogram (EEG), Functional Near-Infrared Spectroscopy (fNIRS), and non-contact, e.g., questionnaires, gaming data, methods. This review focuses on various machine learning techniques, such as support vector machines, random forests, and deep learning models, and their applications in improving the accuracy and efficiency of addiction diagnosis. The use of ML to study physiological signals and behavioral indicators has achieved encouraging results, although there are still limitations in the generality of the models and data acquisition methods. This article compares different ML methods, explores their advantages and limitations, and proposes potential improvements for future research on gaming disorder detection.

Paper Citation

in Harvard Style

Jiang Z. (2024). A Comparative Analysis of Contact and Non-Contact Approaches Using Machine Learning for Gaming Disorder Detection. In Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM; ISBN 978-989-758-738-2, SciTePress, pages 387-391. DOI: 10.5220/0013332600004558

in Bibtex Style

@conference{mlscm24,
author={Zhaochen Jiang},
title={A Comparative Analysis of Contact and Non-Contact Approaches Using Machine Learning for Gaming Disorder Detection},
booktitle={Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM},
year={2024},
pages={387-391},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013332600004558},
isbn={978-989-758-738-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM
TI - A Comparative Analysis of Contact and Non-Contact Approaches Using Machine Learning for Gaming Disorder Detection
SN - 978-989-758-738-2
AU - Jiang Z.
PY - 2024
SP - 387
EP - 391
DO - 10.5220/0013332600004558
PB - SciTePress