Abstract :

Excessive Smart Phone Usage And Mobile Dependency Have Emerged As Growing Behavioral Health Concerns Globally, Impacting Psychological Well-being, Cognitive Focus, And Sleep Quality. This Paper Presents A Machine Learning-based Risk Analysis Framework To Detect, Assess, And Classify Levels Of Mobile Dependency Among Users. Leveraging Behavioral And Usage Patterns Such As Daily Screen Time, App Category Distribution, Pickup Frequency, And Self-reported Psychological Metrics We Preprocessed And Transformed The Feature Space To Train Multiple Supervised Classification Algorithms. We Evaluated Model Performance Across Standard Machine Learning Classifiers, Including Decision Trees, Random Forest, And Logistic Regression, To Identify Behavioral Risk Tiers (e.g., Low, High Risk) Based On User Addiction Condition. The Random Forest Model Achieved The Highest Predictive Accuracy And F1-score, Successfully Isolating Key Predictors Of Addiction Such As Late-night Activity And Notification Responsiveness. The Proposed System Provides An Automated, Objective Tool For Early Risk Identification, Paving The Way For Targeted Digital Wellness Interventions.

Published:

15-9-2026

Issue:

Vol. 26 No. 9 (2026)

Page Nos:

234-240

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite