A Multi-Layer Perceptron Model for Predicting Smartphone Addition Levels

S. Nasrin, M. Fahimunnisa, Aslam Shareef, S. Ananya Jyothi, P. Jasmin

2025

Abstract

Smartphone addiction is a serious issue, with adverse effects on mental health, academic achievement, sleep and social relationships. Conventional self-reported questionnaires tend to be inaccurate and susceptible to bias, requiring automated approaches. This research suggests a machine learning model to forecast smartphone addiction based on behavioral metrics like screen time, app usage, social media usage, call duration, and phone unlock frequency. Psychological variables from well-validated questionnaires are also included to enhance prediction accuracy. Different machine learning algorithms such as Decision Trees, SVM, Random Forests, and Neural Networks are experimented with on a labeled dataset. Accuracy, precision, recall, and F1-score are used to evaluate the models, and the results indicate that ensemble methods such as Random Forests work best. The system allows real-time tracking of smartphone addiction risks. It provides an early intervention proactive approach and management of addiction. It can be implemented into digital health applications for users, educators, and healthcare providers. It ultimately seeks to enable healthier smartphone use and digital well-being.

Paper Citation

in Harvard Style

Nasrin S., Fahimunnisa M., Shareef A., Jyothi S. and Jasmin P. (2025). A Multi-Layer Perceptron Model for Predicting Smartphone Addition Levels. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 482-486. DOI: 10.5220/0013915300004919

in Bibtex Style

@conference{icrdicct`2525,
author={S. Nasrin and M. Fahimunnisa and Aslam Shareef and S. Jyothi and P. Jasmin},
title={A Multi-Layer Perceptron Model for Predicting Smartphone Addition Levels},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={482-486},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013915300004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - A Multi-Layer Perceptron Model for Predicting Smartphone Addition Levels
SN - 978-989-758-777-1
AU - Nasrin S.
AU - Fahimunnisa M.
AU - Shareef A.
AU - Jyothi S.
AU - Jasmin P.
PY - 2025
SP - 482
EP - 486
DO - 10.5220/0013915300004919
PB - SciTePress