مروری بر رویکردهای هوش مصنوعی در تشخیص اختلال بیش فعالی و نقص توجه
محورهای موضوعی : مجله فناوری اطلاعات در طراحی مهندسیطاهره ضیاالدینی 1 , محبوبه هوشمند 2 , سید عابد حسینی 3
1 - گروه مهندسی کامپیوتر، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران.
2 - گروه مهندسی کامپیوتر، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران.
3 - گروه مهندسی برق، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران.
کلید واژه: اختلال بیش فعالی/نقص توجه, هوش مصنوعی, یادگیری ماشین, یادگیری عمیق.,
چکیده مقاله :
بیش فعالی/نقص توجه (ADHD) یک اختلال رفتاری-عصبی شایع در کودکان و نوجوانان است که توجه محدود، بیش فعالی و تکانش گری را به همراه دارد. تشخیص دقیق و به‌موقع این اختلال بسیار مهم است تا بتوان مداخلات مناسب درمانی را ارائه داد. در این میان، رویکردهای مبتنی بر هوش مصنوعی می توانند نقش مهمی در بهبود و تسریع فرآیند تشخیص ایفا کنند. این پژوهش به بررسی استفاده از فناوری های هوش مصنوعی در تشخیص ADHD می‌پردازد. بدین منظور ابتدا به معرفی ویژگی های بالینی و چالش‌های موجود در تشخیص ADHD پرداخته می‌شود، سپس انواع رویکردهای هوش مصنوعی مانند یادگیری ماشین، پردازش زبان طبیعی و یادگیری عمیق در این زمینه بررسی می‌شوند. همچنین مطالعه های موردی و نتایج تجربی استفاده از این فناوری‌ها مرور می‌شوند. درنهایت چالش‌ها و محدودیّت‌های موجود و همچنین چشم‌انداز آینده پژوهش‌ها در این حوزه موردبحث قرار می‌گیرند. این پژوهش مروری بر آخرین پیشرفت‌های علمی در استفاده از هوش مصنوعی برای تشخیص ADHD ارائه می‌دهد و می‌تواند به‌عنوان یک منبع برای متخصصان بالینی و پژوهشگران این حوزه مورداستفاده قرار گیرد.
Attention deficit/hyperactivity disorder (ADHD) is a common neurobehavioral disorder in children and adolescents that involves limited attention, hyperactivity, and impulsivity. Accurate and timely diagnosis of this disorder is very important to provide appropriate therapeutic interventions. Meanwhile, approaches based on artificial intelligence can play an important role in improving and speeding up the diagnosis process. This research examines the use of artificial intelligence technologies in the diagnosis of ADHD. For this purpose, the clinical features and challenges in diagnosing ADHD are first introduced, then various artificial intelligence approaches such as machine learning, natural language processing, and deep learning are examined in this field. Also, case studies and experimental results of using these technologies are reviewed. Finally, the existing challenges and limitations as well as the future prospects of research in this field are discussed. This research provides an overview of the latest scientific advances in the use of artificial intelligence for the diagnosis of ADHD and can be used as a resource for clinicians and researchers in this field.
[1] J. Anuradha, Tisha, V. Ramachandran, K. V. Arulalan, and B. K. Tripathy, “Diagnosis of ADHD using SVM algorithm,” in Proceedings of the Third Annual ACM Bangalore Conference, Bangalore India: ACM, Jan. 2010, pp. 1–4. doi: 10.1145/1754288.1754317.
[2] T. Chen, I. Tachmazidis, S. Batsakis, M. Adamou, E. Papadakis, and G. Antoniou, “Diagnosing attention-deficit hyperactivity disorder (ADHD) using artificial intelligence: a clinical study in the UK,” Frontiers in Psychiatry, vol. 14, p. 1164433, 2023, doi: 10.3389/fpsyt.2023.1164433.
[3] J. Hernández-Capistran, L. N. Sánchez-Morales, G. Alor-Hernández, M. Bustos-López, and J. L. Sánchez-Cervantes, “Machine and Deep Learning Algorithms for ADHD Detection: A Review,” in Innovations in Machine and Deep Learning, vol. 134, G. Rivera, A. Rosete, B. Dorronsoro, and N. Rangel-Valdez, Eds., in Studies in Big Data, vol. 134. , Cham: Springer Nature Switzerland, 2023, pp. 163–191. doi: 10.1007/978-3-031-40688-1_8.
[4] S. Khanna and W. Das, “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, 2020, pp. 51–54, doi: 10.1109/AI4G50087.2020.9311012.
[5] S. Oh et al., “Diagnosis of ADHD using virtual reality and artificial intelligence: an exploratory study of clinical applications,” Frontiers in Psychiatry, vol. 15, p. 1383547, 2024, doi: 10.3389/fpsyt.2024.1383547.
[6] S. Tarnima, P. Sandhya, V. Vani, and A. Bhaumik, “Diagnosis and treatment of attention deficit hyperactivity disorder using artificial intelligence,” in AIP Conference Proceedings, AIP Publishing, 2024.
[7] D. A. R. Lopez, H. Pirim, and D. Grewell, “ADHD Prediction in Children Through Machine Learning Algorithms,” in Emerging Trends and Applications in Artificial Intelligence, vol. 960, F. P. García Márquez, A. Jamil, A. A. Hameed, and I. Segovia Ramírez, Eds., in Lecture Notes in Networks and Systems, vol. 960. , Cham: Springer Nature Switzerland, 2024, pp. 89–100. doi: 10.1007/978-3-031-56728-5_8.
[8] I. Tachmazidis, T. Chen, M. Adamou, and G. Antoniou, “A hybrid AI approach for supporting clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in adults,” Health Inf Sci Syst, vol. 9, no. 1, p. 1, Dec. 2021, doi: 10.1007/s13755-020-00123-7.
[9] J. Peng, M. Debnath, and A. K. Biswas, “Efficacy of novel summation-based synergetic artificial neural network in ADHD diagnosis,” Machine Learning with Applications, vol. 6, p. 100120, 2021, doi: 10.1016/j.mlwa.2021.100120.
[10] T. Chen, G. Antoniou, M. Adamou, I. Tachmazidis, and P. Su, “Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Machine Learning,” Applied Artificial Intelligence, vol. 35, no. 9, pp. 657–669, Jul. 2021, doi: 10.1080/08839514.2021.1933761.
[11] I. Navarro-Soria, J. R. Rico-Juan, R. Juárez-Ruiz De Mier, and R. Lavigne-Cervan, “Prediction of attention deficit hyperactivity disorder based on explainable artificial intelligence,” Applied Neuropsychology: Child, pp. 1–14, Apr. 2024, doi: 10.1080/21622965.2024.2336019.
[12] G. Güney, E. Kisacik, C. KALAYCIOĞLU, and G. Saygili, “Exploring the attention process differentiation of attention deficit hyperactivity disorder (ADHD) symptomatic adults using artificial intelligence onelectroencephalography (EEG) signals,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 29, no. 5, pp. 2312–2325, 2021, doi: 10.3906/elk-2011-3.
[13] X. Chen et al., “Utilizing artificial intelligence-based eye tracking technology for screening ADHD symptoms in children,” Frontiers in Psychiatry, vol. 14, p. 1260031, 2023, doi: 10.3389/fpsyt.2023.1260031.
[14] D. C. Lohani and B. Rana, “ADHD diagnosis using structural brain MRI and personal characteristic data with machine learning framework,” Psychiatry Research: Neuroimaging, vol. 334, p. 111689, 2023, doi: 10.1016/j.pscychresns.2023.111689.
[15] H. Christiansen et al., “Use of machine learning to classify adult ADHD and other conditions based on the Conners’ Adult ADHD Rating Scales,” Scientific reports, vol. 10, no. 1, p. 18871, 2020, doi: 10.1038/s41598-020-75868-y.
[16] G. Wang, W. Li, S. Huang, and Z. Chen, “A Prospective Study of an Early Prediction Model of Attention Deficit Hyperactivity Disorder Based on Artificial Intelligence,” J Atten Disord, vol. 28, no. 3, pp. 302–309, Feb. 2024, doi: 10.1177/10870547231211360.
[17] A. Parashar, N. Kalra, J. Singh, and R. K. Goyal, “Machine learning based framework for classification of children with ADHD and healthy controls,” Intell. Autom. Soft Comput, vol. 28, no. 3, pp. 669–682, 2021, doi: 10.32604/iasc.2021.017478.
[18] F. Amato, M. Di Gregorio, C. Monaco, M. Sebillo, G. Tortora, and G. Vitiello, “Socially assistive robotics combined with artificial intelligence for ADHD,” in 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), IEEE, 2021, pp. 1–6, doi: 10.1109/CCNC49032.2021.9369633.
[19] M. Sheriff and R. Gayathri, “Retracted: An enhanced ensemble machine learning classification method to detect attention deficit hyperactivity for various artificial intelligence and telecommunication applications,” Computational Intelligence, vol. 38, no. 4, pp. 1327–1337, Aug. 2022, doi: 10.1111/coin.12509.
[20] M. S. NV and R. Surendran, “Prediction of Attention Deficit Hyperactivity Disorder (ADHD) in Adult using Novel Artificial Neural Network Algorithm,” in 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), IEEE, 2022, pp. 135–139, 10.1109/ICAISS55157.2022.10010869.
[21] A. Sharma, A. Jain, S. Sharma, A. Gupta, P. Jain, and S. P. Mohanty, “iPAL: A Machine Learning Based Smart Healthcare Framework for Automatic Diagnosis of Attention Deficit/Hyperactivity Disorder,” SN COMPUT. SCI., vol. 5, no. 4, p. 433, Apr. 2024, doi: 10.1007/s42979-024-02779-4.
[22] C. Nash, R. Nair, and S. M. Naqvi, “Machine Learning in ADHD and Depression Mental Health Diagnosis: A Survey,” IEEE Access, vol. 11, pp. 86297-86317, 2023, doi: 10.1109/ACCESS.2023.3304236.
[23] E. Salah, M. Shokair and W. Shalaby, “Effective techniques for classifying ADHD based on artificial intelligence,” 2023 doi: 10.21203/rs.3.rs-2828086/v1.
[24] I.-C. Lin, S.-C. Chang, Y.-J. Huang, T. B. Kuo, and H.-W. Chiu, “Distinguishing different types of attention deficit hyperactivity disorder in children using artificial neural network with clinical intelligent test,” Frontiers in Psychology, vol. 13, p. 1067771, 2023, doi: 10.3389/fpsyg.2022.1067771.
[25] S. Walvekar, B. Thawkar, M. Chintamaneni, and G. Kaur, “Recent Advances of Artificial Intelligence Tools in Attention-Deficit Hyperactivity Disorder (ADHD),” Current Psychopharmacology, vol. 11, no. 1, pp. 18–29, 2022, doi: 10.2174/2211556011666220607112528.
[26] R. Medina et al., “Electrophysiological brain changes associated with cognitive improvement in a pediatric attention deficit hyperactivity disorder digital artificial intelligence-driven intervention: randomized controlled trial,” Journal of medical Internet research, vol. 23, no. 11, p. e25466, 2021, doi: 10.2196/25466.
[27] M. M. Misgar and M. P. S. Bhatia, “Advancing ADHD diagnosis: using machine learning for unveiling ADHD patterns through dimensionality reduction on IoMT actigraphy signals,” Int. j. inf. tecnol., May 2024, doi: 10.1007/s41870-024-01895-x.
[28] M. Moghaddari, M. Z. Lighvan, and S. Danishvar, “Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG,” Computer Methods and Programs in Biomedicine, vol. 197, p. 105738, 2020, doi: 10.1016/j.cmpb.2020.105738.
[29] A. Ahmadi, M. Kashefi, H. Shahrokhi, and M. A. Nazari, “Computer aided diagnosis system using deep convolutional neural networks for ADHD subtypes,” Biomedical Signal Processing and Control, vol. 63, p. 102227, 2021, doi: 10.1016/j.bspc.2020.102227.
[30] P. Amado-Caballero et al., “Objective ADHD diagnosis using convolutional neural networks over daily-life activity records,” IEEE journal of biomedical and health informatics, vol. 24, no. 9, pp. 2690–2700, 2020, doi: 10.1109/JBHI.2020.2964072.
[31] M. Chen, H. Li, J. Wang, J. R. Dillman, N. A. Parikh, and L. He, “A Multichannel Deep Neural Network Model Analyzing Multiscale Functional Brain Connectome Data for Attention Deficit Hyperactivity Disorder Detection,” Radiology: Artificial Intelligence, vol. 2, no. 1, p. e190012, Dec. 2019, doi: 10.1148/ryai.2019190012.
[32] L. Dubreuil-Vall, G. Ruffini, and J. A. Camprodon, “Deep learning convolutional neural networks discriminate adult ADHD from healthy individuals on the basis of event-related spectral EEG,” Frontiers in neuroscience, vol. 14, p. 251, 2020, doi: 10.3389/fnins.2020.00251.
[33] H. Chen, Y. Song, and X. Li, “A deep learning framework for identifying children with ADHD using an EEG-based brain network,” Neurocomputing, vol. 356, pp. 83–96, 2019, doi: 10.1016/j.neucom.2019.04.058.
[34] M. Mafi and S. Radfar, “High dimensional convolutional neural network for EEG connectivity-based diagnosis of ADHD,” Journal of Biomedical Physics & Engineering, vol. 12, no. 6, p. 645, 2022, doi: 10.31661/jbpe.v0i0.2108-1380.
[35] S. A. Hosseini, Y. Modaresnia, and F. A. Torghabeh, “EEG-Based Effective Connectivity Analysis for Attention Deficit Hyperactivity Disorder Detection Using Color-Coded Granger-Causality Images and Custom Convolutional Neural Network,” International Clinical Neuroscience Journal, vol. 10, no. 1, pp. e12–e12, 2023, doi:10.34172/icnj.2023.12.
[36] T. Zhang et al., “Separated channel attention convolutional neural network (SC-CNN-attention) to identify ADHD in multi-site rs-fMRI dataset,” Entropy, vol. 22, no. 8, p. 893, 2020, doi: 10.3390/e22080893.
[37] R. Liu, Z. Huang, M. Jiang, and K. C. Tan, “Multi-LSTM networks for accurate classification of attention deficit hyperactivity disorder from resting-state fMRI data,” in 2020 2nd International Conference on Industrial Artificial Intelligence (IAI), IEEE, 2020, pp. 1–6, doi: 10.1109/IAI50351.2020.9262176.
[38] M. Siniatchkin et al., “Behavioural Treatment Increases Activity in the Cognitive Neuronal Networks in Children with Attention Deficit/Hyperactivity Disorder,” Brain Topogr, vol. 25, no. 3, pp. 332–344, Jul. 2012, doi: 10.1007/s10548-012-0221-6.
[39] M. de Oliveira Meira, A. M. de Paula Canuto, B. M. de Carvalho, and R. L. C. Jales, “Comparison of Machine Learning predictive methods to diagnose the Attention Deficit/Hyperactivity Disorder levels using SPECT,” Research, Society and Development, vol. 11, no. 8, pp. e54811831258–e54811831258, 2022.
[40] L. Zou, J. Zheng, C. Miao, M. J. Mckeown, and Z. J. Wang, “3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI,” Ieee Access, vol. 5, pp. 23626–23636, 2017, doi: 10.1109/ACCESS.2017.2762703.
[41] Z. Wang, Y. Zhu, H. Shi, Y. Zhang, and C. Yan, “A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images,” Math. Biosci. Eng, vol. 18, pp. 6978–6994, 2021, doi: 10.3934/mbe.2021347.
[42] T. W. P. Janssen et al., “Neural network topology in ADHD; evidence for maturational delay and default-mode network alterations,” Clinical neurophysiology, vol. 128, no. 11, pp. 2258–2267, 2017, doi: 10.1016/j.clinph.2017.09.004.
[43] G. Deshpande, P. Wang, D. Rangaprakash, and B. Wilamowski, “Fully connected cascade artificial neural network architecture for attention deficit hyperactivity disorder classification from functional magnetic resonance imaging data,” IEEE transactions on cybernetics, vol. 45, no. 12, pp. 2668–2679, 2015, doi: 10.1109/TCYB.2014.2379621.
[44] M. R. Mohammadi, A. Khaleghi, A. M. Nasrabadi, S. Rafieivand, M. Begol, and H. Zarafshan, “EEG classification of ADHD and normal children using non-linear features and neural network,” Biomed. Eng. Lett., vol. 6, no. 2, pp. 66–73, May 2016, doi: 10.1007/s13534-016-0218-2.
[45] R. H. Pruim et al., “An integrated analysis of neural network correlates of categorical and dimensional models of attention-deficit/hyperactivity disorder,” Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, vol. 4, no. 5, pp. 472–483, 2019, doi: 10.1016/j.bpsc.2018.11.014.
[46] M. Muñoz-Organero, L. Powell, B. Heller, V. Harpin, and J. Parker, “Automatic extraction and detection of characteristic movement patterns in children with ADHD based on a convolutional neural network (CNN) and acceleration images,” Sensors, vol. 18, no. 11, p. 3924, 2018, doi: 10.3390/s18113924.
[47] S. Abdolmaleki and M. S. Abadeh, “Brain MR image classification for ADHD diagnosis using deep neural networks,” in 2020 international conference on machine vision and image processing (MVIP), IEEE, 2020, pp. 1–5, doi: 10.1109/MVIP49855.2020.9116877.
[48] S. Motamed and E. Askari, “Recognition of Attention Deficit/Hyperactivity Disorder (ADHD) Based on Electroencephalographic Signals Using Convolutional Neural Networks (CNNs),” Journal of Information Systems and Telecommunication (JIST), vol. 3, no. 39, p. 222, 2022, doi: 10.52547/jist.16399.10.39.222.
[49] M. Delavarian, E. Nayebi, P. Dibajnia, G.-A. Afrooz, S. Gharibzadeh, and F. Towhidkhah, “Designing an accurate system for differentiating children with attention deficit-hyperactivity disorder from oppositional defiant disorder by using artificial neural network,” The Scientific Journal of Rehabilitation Medicine, vol. 4, no. 1, pp. 90–98, 2015.
[50] A. Nouri and Z. Tabanfar, “Detection of ADHD Disorder in Children Using Layer-Wise Relevance Propagation and Convolutional Neural Network: An EEG Analysis,” Frontiers in Biomedical Technologies, vol. 11, no. 1, pp. 14–21, 2024, doi: 10.18502/fbt.v11i1.14507.
[51] B. A. Rahadian, C. Dewi, and B. Rahayudi, “The performance of genetic algorithm learning vector quantization 2 neural network on identification of the types of attention deficit hyperactivity disorder,” in 2017 International Conference on Sustainable Information Engineering and Technology (SIET), IEEE, 2017, pp. 337–341, doi: 10.1109/SIET.2017.8304160.
[52] S. Altun, A. Alkan, and H. Altun, “Automatic diagnosis of attention deficit hyperactivity disorder with continuous wavelet transform and convolutional neural network,” Clinical Psychopharmacology and Neuroscience, vol. 20, no. 4, p. 715, 2022, doi: 10.9758/cpn.2022.20.4.715.
[53] G. Taşpınar and N. Özkurt, “fMRG Hacimlerini Kullanarak DEHB’nin 3B ESA Tabanlı Otomatik Teşhisi 3D CNN Based Automatic Diagnosis of ADHD Using fMRI Volumes”, vol. 25, no. 73, pp. 1-8, 2023, doi: 10.21205/deufmd.2023257301.
[54] Y. Wang, H. Wang, H. Pirim, L.-W. Fan, and N. Ojeda, “Attention-Deficit/Hyperactivity Disorder Diagnosis with Temporal Diffusion Convolutional Recurrent Neural Networks”, Biomedical Sciences Instrumentation, vol. 58, no. 1, 2022, doi: 10.34107/MYQO345429.
[55] G. Ariyarathne, S. De Silva, S. Dayarathna, D. Meedeniya, and S. Jayarathne, “ADHD Identification using Convolutional Neural Network with Seed-based Approach for fMRI Data,” in Proceedings of the 2020 9th International Conference on Software and Computer Applications, Langkawi Malaysia: ACM, Feb. 2020, pp. 31–35. doi: 10.1145/3384544.3384552.
[56] E. Salah, M. Shokair and A. El-Samie, “Detection Attention Deficit Hyperactivity Disorder by using Convolution Neural Network,” International Journal of Telecommunications, vol. 3, no. 02, pp. 1–11, 2023, doi: 10.21608/ijt.2023.315782.
[57] H. Safari, S. Makvand Hosseini, P. Sabahi, and A. Maleki, “Diagnosis of Attention Deficit/Hyperactivity Disorder with Fourth Wechsler Tool and Integrated Version: Ranking of Effective sub Scale with Artificial Neural Network Analysis,” Neuropsychology, vol. 6, no. 23, pp. 99–122, 2021, doi: 10.30473/clpsy.2020.52924.1546.
[58] C. Uyulan and E. S. Gokten, “Prediction of Long-term Prognosis of Children with Attention-deficit/Hyperactivity Disorder in Conjunction with Deep Neural Network Regression,” Psychiatry and Behavioral Sciences, vol. 12, no. 4, p. 176, 2022, doi: 10.5455/PBS.20220602052257.
[59] N. Qiang, Q. Dong, Y. Sun, B. Ge, and T. Liu, “deep variational autoencoder for modeling functional brain networks and ADHD identification,” in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), IEEE, 2020, pp. 554–557, doi: 10.1109/ISBI45749.2020.9098480
[60] R. Rodriguez-Herrera et al., “Contingency-based flexibility mechanisms through a reinforcement learning model in adults with Attention-Deficit/Hyperactivity Disorder and Obsessive-Compulsive Disorder,” medRxiv, pp. 2024–01, 2024, doi: 10.1101/2024.01.17.24301365.
[61] J. Zupan, “Introduction to artificial neural network (ANN) methods: what they are and how to use them,” Acta Chimica Slovenica, vol. 41, no. 3, p. 327, 1994.
[62] W. D. Fosco, L. W. Hawk, K. S. Rosch, and M. G. Bubnik, “Evaluating cognitive and motivational accounts of greater reinforcement effects among children with attention-deficit/hyperactivity disorder,” Behav Brain Funct, vol. 11, no. 1, p. 20, Dec. 2015, doi: 10.1186/s12993-015-0065-9.
[63] E. J. Sonuga-Barke, “ADHD as a reinforcement disorder–Moving from general effects to identifying (six) specific models to test.,” vol. 52, no. 9, pp. 917-8, 2011, doi: 10.1111/j.1469-7610.2011.02444.x.
[64] M. Luman, G. Tripp, and A. Scheres, “Identifying the neurobiology of altered reinforcement sensitivity in ADHD: a review and research agenda,” Neuroscience & Biobehavioral Reviews, vol. 34, no. 5, pp. 744–754, 2010, doi: 10.1016/j.neubiorev.2009.11.021.
[65] H. De Meyer, T. Beckers, G. Tripp, and S. Van Der Oord, “Reinforcement Contingency Learning in Children with ADHD: Back to the Basics of Behavior Therapy,” J Abnorm Child Psychol, vol. 47, no. 12, pp. 1889–1902, Dec. 2019, doi: 10.1007/s10802-019-00572-z.
[66] A. Sethi, V. Voon, H. D. Critchley, M. Cercignani, and N. A. Harrison, “A neurocomputational account of reward and novelty processing and effects of psychostimulants in attention deficit hyperactivity disorder,” Brain, vol. 141, no. 5, pp. 1545–1557, 2018, doi: 10.1093/brain/awy048.
[67] H. Alkahtani, T. H. Aldhyani, Z. A. Ahmed, and A. A. Alqarni, “Developing System-Based Artificial Intelligence Models for Detecting the Attention Deficit Hyperactivity Disorder,” Mathematics, vol. 11, no. 22, p. 4698, 2023, doi: 10.3390/math11224698.
[68] D. Yu and J. hui Fang, “Using artificial intelligence methods to study the effectiveness of exercise in patients with ADHD,” Frontiers in Neuroscience, vol. 18, p. 1380886, 2024, doi: 10.3389/fnins.2024.1380886.
[69] N. Alsharif, M. H. Al-Adhaileh, and M. Al-Yaari, “Accurate Identification of Attention-deficit/Hyperactivity Disorder Using Machine Learning Approaches,” Journal of Disability Research, vol. 3, no. 1, p. 20230053, 2024, doi: 10.57197/JDR-2023-0053.
