Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 21, 2025Open Access

Machine learning model for prediction of smartphone addiction

View Full Paper
Ask AI
Bookmark
Share

Authors

MJM JanaviSMSeshaiah Merikapudi

Discussion

Loading...

Member takes

Overview

Machine learning models predict smartphone addiction in young adults, suggesting effective intervention strategies.

Key Points

  • Predictive modeling identifies smartphone addiction risks, providing insights into user behavior and engagement.
  • Daily screen usage and social media activity are crucial features, with machine learning showing significant predictive capability.
  • The framework integrates machine learning with interactive visualization tools for real-time feedback on smartphone use.
  • This approach supports healthier smartphone habits, emphasizing the need for early detection and intervention strategies.

Cite This Study

Janavi et al. (2025) studied this question.

synapsesocial.com/papers/68d43afa713b0b5dfea7ab92https://doi.org/10.63363/aijfr.2025.v06i05.1371
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Predicting Social Media Addiction Using Machine Learning and Interactive Visualization with Streamlit2025
  2. 2The Relationship Between Smartphone and Game Addiction, Leisure Time Management, and the Enjoyment of Physical Activity: A Comparison of Regression Analysis and Machine Learning Models2025
  3. 3Conceptually Informed AI/ML for South Korean Adolescent Smartphone Overdependency: Low-Risk Screening, Construct Exploration, & Place-Based Policy Implication Profiles2025
  4. 4Adolescent Smartphone Overdependence in South Korea: A Place-Stratified Evaluation of Conceptually Informed AI/ML Modeling2025
  5. 5Adolescent Smartphone Overdependence in South Korea: A Place-Stratified Evaluation of Conceptually Informed AI/ML Modeling2025