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

Published at

WACV, IJCNN, ICIP, Smart Agricultural Technology

Research Interests

Machine Learning, Computer Vision

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

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

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

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

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

Farhana Sarker, PhD
Farhana Sarker, PhD

Farhana Sarker, PhD

  • Director, WiSE Wing

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

ICDMW, IEEE Access

References

  1. 1.M Duda, R Ma, N Haber, D P Wall. (2016). Use of machine learning for behavioral distinction of autism and ADHD. Translational Psychiatry, 6(2), e732[10.1038/tp.2015.221]
  2. 2.Erik Winther Skogli, Martin H Teicher, Per Normann Andersen, Kjell Tore Hovik, Merete Øie. (2013). ADHD in girls and boys – gender differences in co-existing symptoms and executive function measures. BMC Psychiatry, 13(1), 298[10.1186/1471-244x-13-298]
  3. 3.Florence Mowlem, Jessica Agnew-Blais, Eric Taylor, Philip Asherson. (2018). Do different factors influence whether girls versus boys meet ADHD diagnostic criteria? Sex differences among children with high ADHD symptoms. Psychiatry Research, 272, 765–773[10.1016/j.psychres.2018.12.128]
  4. 4.Samuele Cortese, David Coghill. (2018). Twenty years of research on attention-deficit/hyperactivity disorder (ADHD): looking back, looking forward. Evidence-Based Mental Health, 21(4), 173–176[10.1136/ebmental-2018-300050]
  5. 5.Theresa S. Emser, Blair A. Johnston, J. Douglas Steele, Sandra Kooij, Lisa Thorell, Hanna Christiansen. (2018). Assessing ADHD symptoms in children and adults: evaluating the role of objective measures. Behavioral and Brain Functions, 14(1), 11[10.1186/s12993-018-0143-x]
  6. 6.Sarah O’Neill, Khushmand Rajendran, Shelagh M. Mahbubani, Jeffrey M. Halperin. (2017). Preschool Predictors of ADHD Symptoms and Impairment During Childhood and Adolescence. Current Psychiatry Reports, 19(12), 95[10.1007/s11920-017-0853-z]
  7. 7.Ortal Slobodin, Inbal Yahav, Itai Berger. (2020). A Machine-Based Prediction Model of ADHD Using CPT Data. Frontiers in Human Neuroscience, 14, 560021[10.3389/fnhum.2020.560021]
  8. 8.Md. Maniruzzaman, Md. Al Mehedi Hasan, Nobuyoshi Asai, Jungpil Shin. (2023). Optimal Channels and Features Selection Based ADHD Detection From EEG Signal Using Statistical and Machine Learning Techniques. IEEE Access, 11, 33570–33583[10.1109/access.2023.3264266]
  9. 9.William Das, Shubh Khanna. (2021). A Robust Machine Learning Based Framework for the Automated Detection of ADHD Using Pupillometric Biomarkers and Time Series Analysis. Scientific Reports, 11(1), 16370[10.1038/s41598-021-95673-5]
  10. 10.A. Athanasiadou, J. K. Buitelaar, P. Brovedani, O. Chorna, F. Fulceri, A. Guzzetta, Maria Luisa Scattoni. (2019). Early motor signs of attention-deficit hyperactivity disorder: a systematic review. European Child & Adolescent Psychiatry, 29(7), 903–916[10.1007/s00787-019-01298-5]
  11. 11.Tianhua Chen, Ilias Tachmazidis, Sotiris Batsakis, Marios Adamou, Emmanuel Papadakis, Grigoris Antoniou. (2023). Diagnosing attention-deficit hyperactivity disorder (ADHD) using artificial intelligence: a clinical study in the UK. Frontiers in Psychiatry, 14, 1164433[10.3389/fpsyt.2023.1164433]
  12. 12.Meghan Miller, Anne B. Arnett, Elizabeth Shephard, Tony Charman, Hanna C. Gustafsson, Heather M. Joseph, Sarah Karalunas, Joel T. Nigg, Guilherme V. Polanczyk, Elinor L. Sullivan, Emily J. H. Jones. (2023). Delineating early developmental pathways to ADHD: Setting an international research agenda. JCPP Advances, 3(2), e12144[10.1002/jcv2.12144]
  13. 13.Pietro De Rossi, Italo Pretelli, Deny Menghini, Barbara D’Aiello, Silvia Di Vara, Stefano Vicari. (2022). Gender-Related Clinical Characteristics in Children and Adolescents with ADHD. Journal of Clinical Medicine, 11(2), 385[10.3390/jcm11020385]
  14. 14.Heather M. Joseph, Nicole E. Lorenzo, Nadiyah Fisher, Danielle R. Novick, Cassandra Gibson, Scott D. Rothenberger, Jill E. Foust, Andrea Chronis‐Tuscano. (2023). Research Review: A systematic review and meta‐analysis of infant and toddler temperament as predictors of childhood attention‐deficit/hyperactivity disorder. Journal of Child Psychology and Psychiatry, 64(5), 715–735[10.1111/jcpp.13753]
  15. 15.Tianhua Chen, Grigoris Antoniou, Marios Adamou, Ilias Tachmazidis, Pan Su. (2021). Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Machine Learning. Applied Artificial Intelligence, 35(9), 657–669[10.1080/08839514.2021.1933761]
  16. 16.Mehak Mengi, Deepti Malhotra. (2021). Artificial Intelligence Based Techniques for the Detection of Socio-Behavioral Disorders: A Systematic Review. Archives of Computational Methods in Engineering, 29(5), 2811–2855[10.1007/s11831-021-09682-8]
  17. 17.Amandeep Kaur, Karanjeet Singh Kahlon. (2022). Accurate Identification of ADHD among Adults Using Real-Time Activity Data. Brain Sciences, 12(7), 831[10.3390/brainsci12070831]
  18. 18.Lok Yee Chloe Tam, Yanisa Taechameekietichai, Jennifer L. Allen. (2024). Individual child factors affecting the diagnosis of attention deficit hyperactivity disorder (ADHD) in children and adolescents: a systematic review. European Child & Adolescent Psychiatry, 34(5), 1469–1496[10.1007/s00787-024-02590-9]
  19. 19.Patrick K. Goh, Anjeli R. Elkins, Pevitr S. Bansal, Ashley G. Eng, Michelle M. Martel. (2023). Data-Driven Methods for Predicting ADHD Diagnosis and Related Impairment: The Potential of a Machine Learning Approach. Research on Child and Adolescent Psychopathology, 51(5), 679–691[10.1007/s10802-023-01022-7]
  20. 20.Yang S. Liu, Fernanda Talarico, Dan Metes, Yipeng Song, Mengzhe Wang, Lawrence Kiyang, Dori Wearmouth, Shelly Vik, Yifeng Wei, Yanbo Zhang, Jake Hayward, Ghalib Ahmed, Ashley Gaskin, Russell Greiner, Andrew Greenshaw, Alex Alexander, Magdalena Janus, Bo Cao. (2024). Early identification of children with Attention-Deficit/Hyperactivity Disorder (ADHD). PLOS Digital Health, 3(11), e0000620[10.1371/journal.pdig.0000620]
  21. 21.Ismael Santarrosa-López, Giner Alor-Hernández, Maritza Bustos-López, Jonathan Hernández-Capistrán, Laura Nely Sánchez-Morales, José Luis Sánchez-Cervantes, Humberto Marín-Vega. (2024). DETEC-ADHD: A Data-Driven Web App for Early ADHD Detection Using Machine Learning and Electroencephalography. Big Data and Cognitive Computing, 9(1), 3[10.3390/bdcc9010003]
  22. 22.Davoud Amiri, Lamberto Briziarelli, Enrico Tempesta. (2025). Gender disparities in ADHD medication efficacy: investigating treatment outcomes for females compared to males. Middle East Current Psychiatry, 32(1)[10.1186/s43045-025-00508-y]
  23. 23.Esther Tobarra‐Sanchez, Lucy Riglin, Sharifah S. Agha, Evie Stergiakouli, Anita Thapar, Kate Langley. (2022). Preschool development, temperament and genetic liability as early markers of childhood ADHD: A cohort study. JCPP Advances, 2(3), e12099[10.1002/jcv2.12099]
  24. 24.Kuo-Chung Chu, Hsin-Jou Huang, Yu-Shu Huang. (2023). Validity of Diagnostic Support Model for Attention Deficit Hyperactivity Disorder: A Machine Learning Approach. Journal of Personalized Medicine, 13(11), 1525[10.3390/jpm13111525]
  25. 25.Zineb Namasse, Mohamed Tabaa, Samar Mouchawrab. (2024). Predict ADHD in Children and Adults with Machine Learning Techniques. Procedia Computer Science, 251, 124–131[10.1016/j.procs.2024.11.092]
← All publications