• B Sunil Kumar
• Shivasindu C
• Swathi J
• Yaswanth Yadav T
• Sharook P

The issue of Smartphone addiction has become one of the topics of concern over the last few years, as more people find themselves with excessive use of the device, loss of productivity, and are physically and psychologically affected. Therefore, the need to determine accurate measures of predicting smartphone addiction and finding out who is at risk is increasing. This study applies machine learning to come up with a model that can predict smartphone addiction using the data, which was collected in the survey conducted on smartphone users. Demographic (including age, gender), behavioral (including the usage of smartphones, their usage frequency, time spent on them), and psychological (including stress, anxiety, depression, etc.) information is also enclosed in the dataset. The model is trained using a subset of the data and it is tested on the rest of the data using performance measures like accuracy. According to experimental findings, the proposed model is of high predictive performance. Smartphone usage habits, like the frequency of checking notifications, the time spent at the phone every day, and the most frequently used types of applications, are some of the key aspects of addiction prediction. On the whole, this paper shows that machine learning methods can be effective and applicable in predicting smartphone addiction.

Decision Tree, Random Forest, Logistic Regression, Convolutional Neural Network (CNN), Machine Learning techniques.

"Predicting Smartphone Addiction Using A Machine Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 2, page no.c282-c287, February-2026, Available :http://www.jetir.org/papers/JETIR2602240.pdf

"Predicting Smartphone Addiction Using A Machine Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 2, page no. ppc282-c287, February-2026, Available at : http://www.jetir.org/papers/JETIR2602240.pdf