Alim A, Imtiaz MH (2023) Automatic identification of children with adhd from eeg brain waves. Signals 4:193–205

Article  Google Scholar 

Anusha M, Thangam R, Dubey SK (2024) 1. quantum neural network for image feature extraction using mnist https://doi.org/10.1109/i-smac61858.2024.10714726

Baji I, Turi A, Nagy DL, Sterczer A (2023) Attention deficit hyperactivity disorder (ADHD) syndrome across ages. Dev Health Sci. https://doi.org/10.1556/2066.2023.00050

Article  Google Scholar 

Bellec P, Chu C, Chouinard-Decorte F, Benhajali Y, Margulies DS, Craddock RC (2017) The neuro bureau ADHD-200 preprocessed repository. Neuroimage 144:275–286

Article  PubMed  Google Scholar 

Cao M, Martin E, Li X (2023) Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms. Transl Psychiatry 13:236

Article  PubMed  PubMed Central  Google Scholar 

Chen H, Song Y, Li X (2019) A deep learning framework for identifying children with ADHD using an EEG-based brain network. Neurocomputing 356:83–96

Article  Google Scholar 

Consortium A (2012) The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front Syst Neurosci 6:62

Article  Google Scholar 

Derkman M, Roos S, van Tetering E (2024) Adhd. Kind en Adolescent Praktijkreeks. https://doi.org/10.1007/978-90-368-2923-6_1

Elkhodary HO, Youssef SM (2024) An improved model for the diagnosis of attention deficit/hyperactivity disorder (adhd) using resting-state fmri data. In: 2024 International Conference on Machine Intelligence and Smart Innovation (ICMISI), IEEE. pp. 60–63

Gao MS, Tsai FS, Lee CC (2020) Learning a phenotypic-attribute attentional brain connectivity embedding for ADHD classification using rs-fmri. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE. pp. 5472–5475

Gao R, Deng K, Xie M (2022) Deep learning-assisted ADHD diagnosis. https://doi.org/10.1145/3570773.3570849

Ghiassian S, Greiner R, Jin P, Brown MR (2016) Using functional or structural magnetic resonance images and personal characteristic data to identify ADHD and autism. PLoS ONE 11:e0166934

Article  PubMed  PubMed Central  Google Scholar 

Gouri MH, Kumar MA (2025) Automated machine learning: empowering data-driven decisions with tpot and auto-sklearn

Gu X, Dang C, Shi T, Tang L, Wang K, Luo X, Zhu Y, Feng Y, Wu G, Zou L et al (2024) A novel brain network analysis method for pediatric ADHD using RFE-ga feature selection strategy. Biomed Phys Eng Express 10:065038

Article  Google Scholar 

Gülhan PG, Özmen G (2024) The use of fmri regional analysis to automatically detect ADHD through a 3d CNN-based approach. J Imag Info Med, pp 1–14

Hernández-Capistran J, Sánchez-Morales LN, Alor-Hernández G, Bustos-López M, Sánchez-Cervantes JL (2023) Machine and deep learning algorithms for ADHD detection: a review. Innovations in Machine and Deep Learning: Case Stu Appl, pp 163–191

Hosseini E, Hosseini SA, Servaes S, Hall B, Rosa-Neto P, Moradi AR, Kumar A, Pedram MM, Chawla S (2025) Transforming 3d MRI to 2d feature maps using pre-trained models for diagnosis of attention deficit hyperactivity disorder. Tomography 11:56. https://doi.org/10.3390/tomography11050056

Article  PubMed  PubMed Central  Google Scholar 

Kaur A, Kahlon KS (2022) Accurate identification of ADHD among adults using real-time activity data. Brain Sci 12:831

Article  PubMed  PubMed Central  Google Scholar 

Khanna S, Das W (2020) A novel application for the efficient and accessible diagnosis of ADHD using machine learning. In: 2020 IEEE/ITU International Conference on Artificial Intelligence for Good (AI4G), IEEE. pp. 51–54

Kuang D, He L (2014) Classification of ADHD with deep learning. In: 2014 International Conference on cloud computing and big data, IEEE. pp. 27–32

Le TT, Fu W, Moore JH (2018) Scaling tree-based automated machine learning to biomedical big data with a dataset selector. BioRxiv , 502484

Le TT, Fu W, Moore JH (2020) Scaling tree-based automated machine learning to biomedical big data with a feature set selector. Bioinformatics 36:250–256

Article  PubMed  CAS  Google Scholar 

Li S, Sun Y, Nair R, Naqvi SM (2023) Enhancing ADHD detection using diva interview-based audio signals and a two-stream network. In: 2023 IEEE International Performance, Computing, and Communications Conference (IPCCC), IEEE. pp. 291–296

Little RJ, Rubin DB (2002) Statistical analysis with missing data, 2nd edn. Wiley-Interscience

Book  Google Scholar 

Lohani DC, Rana B (2024) Role of personal characteristics data for classification of attention-deficit hyperactivity disorder. Intell Decis Technol 18:2559–2575

Google Scholar 

Mahesh T, Goswami T, Sriramulu S, Sharma N, Kumari A, Khekare G (2022) Cognitive based attention deficit hyperactivity disorder detection with ability assessment using auto encoder based hidden markov model. Int J Commun Netw Inform Secur 14:53–65

Article  Google Scholar 

Maniruzzaman M, Shin J, Hasan MAM (2022a) Predicting children with ADHD using behavioral activity: a machine learning analysis. Appl Sci 12:2737

Article  CAS  Google Scholar 

Maniruzzaman M, Shin J, Hasan MAM, Yasumura A (2022b) Efficient feature selection and machine learning based ADHD detection using EEG signal. Comput Mater Continua 72

Mohan S, Padmashree T (2023) An innovative framework for classification of adhd using machine learning algorithm. In: 2023 2nd International Conference on Automation. IEEE, Computing and Renewable Systems (ICACRS), pp 1016–1019

Moslehi F, Haeri A (2020) 2. An evolutionary computation-based approach for feature selection. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/S12652-019-01570-1

Article  Google Scholar 

Mu S, Wu H, Zhang J, Chang C (2022) Structural brain changes and associated symptoms of ADHD subtypes in children. Cereb Cortex 32:1152–1158

Article  PubMed  Google Scholar 

National Institutes of Health (2024) Mental health and mental disorders. PMC https://pmc.ncbi.nlm.nih.gov/articles/PMC10460242/. Accessed 18 Nov 2024

Olivetti E, Greiner S, Avesani P (2012) ADHD diagnosis from multiple data sources with batch effects. Front Syst Neurosci 6:70

Article  PubMed  PubMed Central  Google Scholar 

Ostojic D, Lalousis PA, Donohoe G, Morris DW (2024) The challenges of using machine learning models in psychiatric research and clinical practice. Eur Neuropsychopharmacol 88:53–65

Article  PubMed  CAS  Google Scholar 

Park Y (2024) 1. Automated machine learning with r: Automl tools for beginners in clinical research. J Minim Invasive Surg. https://doi.org/10.7602/jmis.2024.27.3.129

Article  PubMed  PubMed Central  Google Scholar 

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

Google Scholar 

Peng X, Lin P, Zhang T, Wang J (2013) Extreme learning machine-based classification of ADHD using brain structural MRI data. PLoS One 8:e79476

Article  PubMed  PubMed Central  Google Scholar 

Peterson BS, Trampush J, Brown M, Maglione M, Bolshakova M, Rozelle M, Miles J, Pakdaman S, Yagyu S, Motala A et al (2024) Tools for the diagnosis of ADHD in children and adolescents: a systematic review. Pediatrics 153:e2024065854

Article  PubMed  Google Scholar 

Qureshi MNI, Shafiq M, Memon A, Akbar S (2017) Multi-modal, multi-measure, and multi-class discrimination of ADHD with hierarchical feature extraction and extreme learning machine using structural and functional brain mri. Comput Biol Med 87:74–81

Google Scholar 

Raghuwanshi KS (2018) 5. A qualitative review of two evolutionary algorithms inspired by heuristic population based search methods: Ga & pso. https://doi.org/10.1007/978-981-10-6916-1_15

Reale L, Bartoli B, Cartabia M, Zanetti M, Costantino MA, Canevini MP, Termine C, Bonati M (2017) Comorbidity prevalence and treatment outcome in children and adolescents with ADHD. Eur Child Adolesc Psychiatry 26:1443–1457

Article  PubMed  Google Scholar 

Rosário AT, Boechat AC (2024) 1. How automated machine learning can boost business https://doi.org/10.20944/preprints202409.0426.v1

Rout S, Mallick R, Sahu SK (2023) 4. Exploring the significance of feature analysis in ai/ml modeling. https://doi.org/10.1109/ocit59427.2023.10431396

Salman SA, Lian Z, Ahvanooey MT, Takahashi H, Zhang Y (2022) Classification of ADHD patients using kernel hierarchical extreme learning machine. arXiv preprint arXiv:2206.13761

Usha Rupni K, Aruna Priya P (2023) Identification of attention-deficit-hyperactivity disorder subtypes based on structural MRI grey matter volume and phenotypic information. Curr Med Imaging 19:1656–1664

Google Scholar 

Wang P, Zhao X, Zhong J, Zhou Y (2021) Localization and diagnosis of attention-deficit/hyperactivity disorder. Healthcare 9:372. https://doi.org/10.3390/healthcare9040372

Article  PubMed  PubMed Central  Google Scholar 

World Health Organization (2024) Mental disorders: key facts. https://www.who.int/news-room/fact-sheets/detail/mental-disorders. Accessed 18 Nov 2024

Yang CHH, Qi J, Chen SYC, Chen PY, Siniscalchi SM, Ma X, Lee CH (2021) 2. Decentralizing feature extraction with quantum convolutional neural network for automatic speech recognition. https://doi.org/10.1109/ICASSP39728.2021.9413453

Zhang Y, Kong M, Zhao T, Hong W, Di X, Wang C, Yang R, Li R, Zhu Q (2021) Auxiliary diagnostic system for ADHD in children based on ai technology. J Zhejiang Univ Sci C. https://doi.org/10.1631/FITEE.1900729

Article  Google Scholar 

Zou L, Zheng J, Miao C, Mckeown MJ, Wang ZJ (2017) 3d CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI. Ieee Access 5:23626–23636

Article  Google Scholar