International Journal of Scientific Research and Engineering Development

Addiction to smartphones is one of the growing issues among all other behavioral and psychological disorders since everyone today uses his or her smartphone not only to make calls but also for studying, entertainment, and social communication. In addition, smartphones affect people's psychology, physical condition, studies, and interaction with others. Thus, research is required in order to develop effective strategies for identifying the patterns of addiction to smartphones. The paper suggests developing a Behavioral Data Analysis-Based Smartphone Addiction Prediction and Intervention System using AddictNet and CatBoost algorithms. Using these two algorithms, the developed system will be able to identify users' level of addiction and offer relevant interventions based on analyzing the following indicators: screen time, applications usage, notifications control, and nocturnal phone use. With the help of the CatBoost algorithm, the system will be capable of predicting the level of addiction by dividing users into Low, Moderate, and High-risk groups. Besides, the system will send warnings, notify parents about problematic phone use by their children, and offer users some recommendations regarding their healthy behavior. The combination of behavioral analysis, machine learning, and interventions will allow timely detecting any potential risks of addiction and preventing them. Moreover, the developed system will provide its users with an online platform where they will be able to monitor and change their smartphone behavior.

V.Udhayakumar, D.Venkatraman,"Smartphone Addiction Prediction and Intervention System Using AddictNet and CatBoost" International Journal of Scientific Research and Engineering Development, V9(3): Page(2029-2034) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.