A Comprehensive Review of ADHD: Insights and Technological Interventions in Diagnosis
DOI:
https://doi.org/10.69980/ajpr.v28i4.295Keywords:
Attention Deficit Hyperactivity Disorder, Machine Learning, Deep Learning, Classification PerformancesAbstract
Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder that typically begins in childhood and can persist into adulthood. It is characterized by symptoms of inattention, hyperactivity and impulsivity which can interfere with daily functioning and development. The exact cause of ADHD is not fully understood but is believed to involve genetic, neurological and environmental factors. Diagnosis is typically based on clinical evaluation, behavioral observations and standardized rating scales. The proposed paper aims to conduct a comprehensive review of various Machine Learning (ML) and Deep Learning (DL) techniques applied in the analysis of ADHD as presented in existing literature. It focuses on evaluating and comparing the performance of these techniques to understand their effectiveness. Also the paper discusses different data acquisition modalities such as EEG, MRI, fMRI and others used in ADHD research. By examining these modalities the study highlights their role in supporting accurate diagnosis and analysis. The paper concludes with a statistical summary and performance comparison of the reviewed techniques to offer insights into current trends and potential future directions.
References
1. Lange, Klaus W., et al. "The history of attention deficit hyperactivity disorder." ADHD Attention Deficit and Hyperactivity Disorders 2 (2010): 241-255.
2. Wender, Paul H., Lorraine E. Wolf, and Jeanette Wasserstein. "Adults with ADHD: An overview." Annals of the New York academy of sciences 931.1 (2001): 1-16.
3. Williams, Tamara, et al. "An item-level systematic review of the presentation of ADHD in females." Neuroscience & Biobehavioral Reviews (2025): 106064.
4. Harrison, Caleb, et al. "Prediabetes prevalence by adverse social determinants of health in adolescents." JAMA Network Open 7.6 (2024): e2416088-e2416088.
5. Martin, Joanna. "Why are females less likely to be diagnosed with ADHD in childhood than males?." The Lancet Psychiatry 11.4 (2024): 303-310.
6. Xi, Xiaojian, et al. "The effect of high-order interactions on the functional brain networks of boys with ADHD." The European Physical Journal Special Topics 233.4 (2024): 817-829.
7. Berrezueta-Guzman, Santiago, et al. "Future of ADHD care: Evaluating the efficacy of ChatGPT in therapy enhancement." Healthcare. Vol. 12. No. 6. MDPI, 2024.
8. Schippers, Lessa M., C. U. Greven, and M. Hoogman. "Associations between ADHD traits and self-reported strengths in the general population." Comprehensive Psychiatry 130 (2024): 152461.
9. Van Der Putten, W. J., et al. "Is camouflaging unique for autism? A comparison of camouflaging between adults with autism and ADHD." Autism Research 17.4 (2024): 812-823.
10. American Psychiatric Association “Diagnostic and Statistical Manual of Mental Disorders” (1994)
11. Khare, Smith K., and U. Rajendra Acharya. "An explainable and interpretable model for attention deficit hyperactivity disorder in children using EEG signals." Computers in biology and medicine 155 (2023): 106676.
12. Tenev, Aleksandar, et al. "Machine learning approach for classification of ADHD adults." International Journal of Psychophysiology 93.1 (2014): 162-166.
13. Cao, Meng, Elizabeth Martin, and Xiaobo Li. "Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms." Translational Psychiatry 13.1 (2023): 236.
14. Christiansen, Hanna, et al. "Use of machine learning to classify adult ADHD and other conditions based on the Conners’ Adult ADHD Rating Scales." Scientific reports 10.1 (2020): 18871.
15. Kaur, Amandeep, and Karanjeet Singh Kahlon. "Accurate identification of ADHD among adults using real-time activity data." Brain sciences 12.7 (2022): 831.
16. Alsharif, Nizar, Mosleh Hmoud Al-Adhaileh, and Mohammed Al-Yaari. "Accurate Identification of Attention-deficit/Hyperactivity Disorder Using Machine Learning Approaches." Journal of Disability Research 3.1 (2024): 20230053.
17. Ghasemi, Elham, Mansour Ebrahimi, and Esmaeil Ebrahimie. "Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials." Cognitive Neurodynamics 16.6 (2022): 1335-1349.
18. Mikolas, Pavol, et al. "Training a machine learning classifier to identify ADHD based on real-world clinical data from medical records." Scientific reports 12.1 (2022): 12934.
19. Taspinar, Gurcan, and Nalan Ozkurt. "A review of ADHD detection studies with machine learning methods using rsfMRI data." NMR in Biomedicine 37.8 (2024): e5138.
20. Rohini, B. R., Kamal Shoaib, and H. K. Yogish. "A review on machine learning approaches in diagnosis of ADHD based on big data." Big Data Computing (2024): 281-297.
21. Rasi, Ayshin. The classification of ADHD using machine learning and EEG data: a systematic review. Diss. University of Tasmania, 2024.
22. Deshmukh, Manjusha, Mahi Khemchandani, and Paramjit Mahesh Thakur. "Contributions of brain regions to machine learning-based classifications of attention deficit hyperactivity disorder (ADHD) utilizing EEG signals." Applied Neuropsychology: Adult (2024): 1-15.
23. Ji, Yanqing, Janet Zhang-Lea, and John Tran. "Automated ADHD Detection Using Dual-Modal Sensory Data and Machine Learning." Medical Engineering & Physics (2025): 104328.
24. Kim, Junwon, Seungheon Yang, and Sooin Han. "A MACHINE LEARNING APPROACH TO PREDICT THE THERAPEUTIC EFFICACY OF MOBILE NEUROFEEDBACK IN CHILDREN WITH ADHD." International Journal of Neuropsychopharmacology 28 (2025).
25. Choi, Hangnyoung, et al. "Retinal fundus imaging as biomarker for ADHD using machine learning for screening and visual attention stratification." npj Digital Medicine 8.1 (2025): 164.
26. De Silva, T. I. U. "Machine Learning for Early Detection of Attention Deficit Hyperactivity Disorder (ADHD) In University Students."
27. Sindhu, T. N., and S. R. Sujatha. "Comprehensive Analysis of Attention Deficit/Hyperactivity Disorder using Machine Learning and Deep Learning Algorithms." 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025.
28. Begum, Zarina, and Kareemulla Shaik. "Optimized Temporal Denoised Convolutional Autoencoder for Enhanced ADHD Classification using fMRI Data." IEEE Access (2025).
29. Zaheer, Amna, and Ahmad Akhtar. "Artificial intelligence as a support to diagnose ADHD: an insight of unorthodox approaches: a scoping review." Child Neuropsychology (2025): 1-35.
30. Alsharif, Nizar, et al. "ADHD Diagnosis Using Text Features and Predictive Machine Learning and Deep Learning Algorithms." Journal of Disability Research 3.7 (2024): 20240082.
31. Alsharif, Nizar, Mosleh Hmoud Al-Adhaileh, and Mohammed Al-Yaari. "Diagnosis of attention deficit hyperactivity disorder: A deep learning approach." AIMS Mathematics 9.5 (2024): 10580-10608.
32. Fink, Nora. "Deep Learning–Based 3D Convolutional Neural Networks for ADHD Classification Achieving Over 60% Accuracy: A Comprehensive Study." (2025).
33. Rehman, Abdul, Jerry Chun‐Wei Lin, and Ilona Heldal. "Enhancing Psychologists' Understanding through Explainable Deep Learning Framework for ADHD Diagnosis." Expert Systems 42.2 (2025): e13788.
34. Salah, Eman, et al. "A High-Precision FMRI-CNN Framework with Advanced Classification Techniques for Improved ADHD Diagnosis." International Journal of Engineering and Applied Sciences-October 6 University 2.1 (2025): 68-79.
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