Conference Paper2025

Machine Learning-Based Prediction of ADHD in Bangladeshi Children Using Behavioral and Demographic Insights

Sharbany Panday, Sourish Bairagi, Sinthia Sayma Rahman, Mir Sazzat Hossain, Md Rashedur Rahman, Ashraful Islam, Farhana Sarker

2025 28th International Conference on Computer and Information Technology (ICCIT)

IEEE, pp. 3518-3523

  • Good Health and Well-being — Relevance: 95%
DOI: 10.1109/iccit68739.2025.11491496URL

Abstract

ADHD is a prevalent neurodevelopmental problem, but in Bangladesh, early recognition is not only minimized due to low awareness but also because of limited diagnostic accessibility. This study seeks to close that gap by constructing a DSM-5-informed questionnaire designed to identify behavioral, familial, and socioeconomic antecedents of ADHD in childhood. Participants were school-aged children from various schools, ranging from 4 to 17 years old, and aggregated for a systematized machine learning pipeline. The most predictive variables were tagged using feature selection through inception of classical classifiers like Random Forest, Support Vector Machine, Logistic Regression, and Decision Tree. The study shows demographical and attentional features of importance in predicting the risk of ADHD. Ensemble techniques were the most stable among all models, suggesting that the screening questionnaire may offer useful classification. Although the findings suggest potential for using surveys and machine learning to provide a cost-effective early screen, this research is limited by a small sample size, imbalanced demographics, and a reliance on parent/caregiver/teacher reports. In the future, increasing and including gender-sensitive tools will be crucial to scale up culturally sensitive ADHD detection in Bangladesh.

Keywords

  • attention deficit hyperactivity disorder
  • Bangladeshi children
  • socioeconomic factors
  • DSM-5
  • parent and teacher ratings
  • Feature selection
  • ensemble classifiers
  • early diagnosis

CCDS Authors

Mir Sazzat Hossain
Mir Sazzat Hossain

Mir Sazzat Hossain

  • Research Assistant, CCDS

  • Bachelors

    Independent University, Bangladesh (IUB)

Published at

WACV, IJCNN, ICIP, Smart Agricultural Technology

Md Rashedur Rahman, D. Eng.
Md. Rashedur Rahman, D. Eng.

Md Rashedur Rahman, D. Eng.

  • Co-Director, MIRA & NEST Wings

  • Assistant Professor

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Published at

CHI, HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, Pattern Recognition, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Farhana Sarker, PhD
Farhana Sarker, PhD

Farhana Sarker, PhD

  • Director, WiSE Wing

  • Associate Professor

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Published at

ICDMW, IEEE Access

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