Modeling and Predicting Smartphone Addiction Among Filipinos Using Machine Learning Techniques

Author(s)	Mr. Justin Geo Raro Namilit, Mr. Reymart Padrique Rein, Ms. Nicke Jane Cantila Lisondra, Ms. Eda Luzgapa Dela Cruz
Country	Philippines
Abstract	This study uses machine learning techniques to predict smartphone addiction among Filipinos. As smartphones become more widely used, concerns about digital addiction have grown in significance due to the behavioral and social effects they have. In this study, a set of data is analyzed, including metrics related to smartphone usage, such as time spent on the device and frequency of use of phone applications. The study employs algorithms including J48 Decision Tree, Naïve Bayes, Random Forest, and PART to perform classification utilizing the WEKA data mining system. The aforementioned data show that the Random Forest model's prediction accuracy for smartphone addiction is quite good, as it is more than the accuracy of other models' predictions. Some of the most important factors that affect smartphone addiction are extended screen time, using the phone for social networking, and excessive phone use. It is clear from the aforementioned findings that human behavior has a big impact on smartphone addiction.
Keywords	Smartphone, Machine Learning, WEKA, Digital Addiction, Social Networking
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-17
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.78511

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International Journal For Multidisciplinary Research