Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Machine learning model for prediction of smartphone addiction

Author(s) Ms. Janavi M, Dr. Seshaiah Merikapudi
Country India
Abstract Smartphone addiction has emerged a critical issue affecting adolescents and young adults, often resulting in academic decline, psychological stress, and reduced well- being. Excessive engagement in activities such as social media, gaming, and prolonged screen exposure contributes significantly to this problem. Machine learning methods such as Random Forest, Logistic Regression, and Support Vector Machines (SVM) to predict smartphone addiction based on quantifiable features like daily screen usage, social media activity, gaming time, and the count of installed apps. An interactive dashboard built with Streamlit and Altair is integrated to visualize results and provide real-time feedback. Furthermore, recent advances in behavioral analytics, explainable AI, and digital wellness technologies highlight the growing potential for intelligent systems to enable early detection and intervention. The proposed framework contributes to supporting healthier smartphone use by combining predictive modeling with interpretable visualization tools.
Keywords Smartphone overuse, machine learning, Random Forest, Logistic Regression, Support Vector Machines (SVM), daily screen usage, social media engagement, gaming time, predictive analytics, explainable artificial intelligence (XAI), digital well-being
Field Engineering
Published In Volume 6, Issue 5, September-October 2025
Published On 2025-09-19
DOI https://doi.org/10.63363/aijfr.2025.v06i05.1371
Short DOI https://doi.org/g938mj
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E-ISSN 3048-7641
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