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Classifying Internet Addiction Using Machine Learning Approach: A Study Among Adolescents in Bangladesh.

Akher Ali¹, Md Sahadat Hosain², Md Abu Bakkar Siddik^3,4,5

¹Department of Statistics and Data Science Jahangirnagar University, Savar Dhaka Bangladesh.

Public Health Challenges

|November 17, 2025

Summary

This summary is machine-generated.

Internet addiction (IA) is a growing concern among adolescents. Machine learning identified key risk factors like depression and loneliness, enabling better prevention strategies.

Keywords:

confusion matrix cross‐validation feature selection internet addiction machine learning receiver operating characteristic (ROC)

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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

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Published on: May 31, 2019

Area of Science:

Adolescent Health
Mental Health Technology
Data Science in Healthcare

Background:

Internet addiction (IA) poses significant risks to adolescent mental, emotional, social, and physical well-being.
Adolescents are particularly vulnerable to online temptations due to developmental factors.
Limited traditional research exists on IA in Bangladesh, highlighting a need for novel approaches.

Purpose of the Study:

To identify risk factors associated with Internet addiction (IA) in adolescents.
To leverage advanced machine learning (ML) techniques for IA classification.
To address the research gap in understanding IA prevalence and predictors in Bangladesh.

Main Methods:

Convenience sampling of 385 adolescents surveyed for depression (PHQ-9), loneliness (UCLA-3), and IA (IAT-20).
Boruta feature selection identified key IA prevalence factors.
Evaluated multiple ML classification models including SVM, DT, LR, and RF using cross-validation and ROC curves.

Main Results:

A significant one-third (30.1%) of respondents reported IA.
Key predictors for IA included father's education, favorite activity, loneliness, smoking status, depression, and internet usage duration.
The SVM linear kernel model demonstrated superior performance in classifying IA, achieving high accuracy (0.819) and AUC (0.890).

Conclusions:

Raising awareness about IA among adolescents and parents is critical due to its high prevalence.
ML frameworks effectively identify prognostic indicators for IA, aiding in accurate classification and intervention.
Findings can inform policy-making and counseling services to combat the adolescent IA crisis.