Machine Learning Predictive Model for ADHD Diagnosis: Presentations and Comorbidities
DOI:
https://doi.org/10.13129/2282-1619/mjcp-5647Keywords:
ADHD, Clinical psychology, Comorbidity, Machine learning, Multiclass classification, Psychometric testingAbstract
Background: Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition with heterogeneity and clinical variability. It has three diagnostic presentations (inattentive, hyperactive/impulsive, combined). It is common for it to occur concomitantly with other psychopathologies (neurodevelopmental disorder or behavioral disorder), which increases the complexity of its detection.
Methods: This work developed and evaluated a multiclass machine learning (ML) model to identify ADHD and comorbidities in a pediatric population. The research approach was quantitative and cross-sectional, with a non-experimental design and non-probability sampling. The sample has 892 children aged [6-12] years from Medellín, Colombia (ADHD=780, typically developing controls=112). Cognitive and behavioral assessments were used as predictor variables. Python 3.12.13 was used in the programming algorithms; a computational pipeline was implemented with preprocessing techniques, class balancing using Synthetic Minority Over-sampling Technique (SMOTE) and stratified cross-validation.
Results: Multiple classification algorithms were evaluated, Random Forest (RF) was identified as the optimal model (accuracy=.9778, precision=.9798, recall=.9778, F1-score=.9777; weighted averages for multiclass metrics). The results indicate an adequate capacity of the model to differentiate complex clinical profiles with a performance superior to that of traditional binary classification methods.
Conclusions: Use of accessible and clinically interpretable psychometric variables enhances the applicability of the tool in real-world healthcare settings with limited access to resources and specialized professionals. ML is a promising complementary tool for ADHD detection. External validation is needed to confirm its generalizability across different populations and clinical contexts.
References
1. Agarwal, S., Raj, A., Chowdhury, A., Aich, G., Chatterjee, R., & Ghosh, K. (2024). Investigating the impact of standard brain atlases and connectivity measures on the accuracy of ADHD detection from fMRI data using deep learning. Multimedia Tools and Applications, 83(25), 67023–67057.
https://doi.org/10.1007/s11042-023-17962-7
2. Akrami, L., Malekpour, M., & Abedi, A. (2021). Developing and accessing psychometric properties of the Persian version of Behavior Assessment System for Children (3rd Edition) in children with mild intellectual disabilities and normal children. Iranian Journal of Psychiatry and Clinical Psychology, 27(3), 388–405.
https://doi.org/10.32598/ijpcp.27.4.3462.1
3. Alqaysi, M. E., Albahri, A. S., & Hamid, R. A. (2022). Diagnosis‐based hybridization of multimedical tests and sociodemographic characteristics of autism spectrum disorder using artificial intelligence and machine learning techniques: a systematic review. International Journal of Telemedicine and Applications, 2022(1), 3551528.
https://doi.org/10.1155/2022/3551528
4. Alsharif, N., Al-Adhaileh, M. H., Alsubari, S. N., & Al-Yaari, M. (2024). ADHD diagnosis using text features and predictive machine learning and deep learning algorithms. Journal of Disability Research, 3(7), 20240082.
https://doi.org/10.57197/JDR-2024-0082
5. American Psychiatric Association. (2022). Diagnostic and statistical manual of mental disorders (5th ed., text rev.; DSM-5-TR). American Psychiatric Publishing.
6. Arán Filippetti, V., Gutierrez, M., & Krumm, G. (2024). Norms, convergent validity, test–retest reliability, and practice effects for verbal fluency overall performance, clustering, and switching in Spanish-speaking children. The Clinical Neuropsychologist, 38(7), 1726–1755. https://doi.org/10.1080/13854046.2024.2315729
7. Ashraf, I., Jung, S., Hur, S., & Park, Y. (2024). A systematic literature review of neuroimaging coupled with machine learning approaches for diagnosis of attention deficit hyperactivity disorder. Journal of Big Data, 11(1), 140. https://doi.org/10.1186/s40537-024-00998-3
8. Beauchamp, M. L., Amorim, K., Wunderlich, S. N., Lai, J., Scorah, J., & Elsabbagh, M. (2022). Barriers to access and utilization of healthcare services for minority-language speakers with neurodevelopmental disorders: A scoping review. Frontiers in Psychiatry, 13, 915999. https://doi.org/10.3389/fpsyt.2022.915999
9. Begum, Z., & Shaik, K. (2025). Optimized temporal denoised convolutional autoencoder for enhanced ADHD classification using fMRI data. IEEE Access, 13, 29031–29045.
https://doi.org/10.1109/ACCESS.2025.3539706
10. Bigler, E. D., Allder, S., & Victoroff, J. (2024). What traditional neuropsychological assessment got wrong about mild traumatic brain injury. II: limitations in test development, research design, statistical and psychometric issues. Brain Injury, 38(13), 1053–1074. https://doi.org/10.1080/02699052.2024.2376261
11. Bledsoe, J. C., Xiao, C., Chaovalitwongse, A., Mehta, S., Grabowski, T. J., Semrud-Clikeman, M., Pliszka, S., & Breiger, D. (2020). Diagnostic Classification of ADHD Versus Control: Support Vector Machine Classification Using Brief Neuropsychological Assessment. Journal of Attention Disorders, 24(11), 1547–1556.
https://doi.org/10.1177/1087054716649666
12. Bonti, E., Zerva, I. K., Koundourou, C., & Sofologi, M. (2024). The high rates of comorbidity among neurodevelopmental disorders: reconsidering the clinical utility of distinct diagnostic categories. Journal of Personalized Medicine, 14(3), 300. https://doi.org/10.3390/jpm14030300
13. Caselles-Pina, L., Quesada‐López, A., Sújar, A., Hernandez, E. M. G., & Delgado‐Gómez, D. (2024). A systematic review on the application of machine learning models in psychometric questionnaires for the diagnosis of attention deficit hyperactivity disorder. European Journal of Neuroscience, 60(3), 4115–4127.
https://doi.org/10.1111/ejn.16288
14. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953
15. Chen, L., Yang, Y., Yu, A., Guo, S., Ren, K., Liu, Q., & Qiao, C. (2025). An explainable spatio-temporal graph convolutional network for the biomarkers identification of ADHD. Biomedical Signal Processing and Control, 99, 106913. https://doi.org/10.1016/j.bspc.2024.106913
16. Conners, C. K. (1998). Conners’ Rating Scales–Revised: Technical manual. Multi-Health Systems.
17. da Costa, D. I., de Souza, R. V., & Fiamoncini, T. C. (2026). The role of neuropsychological assessment in the investigation of neurodevelopmental disorders. Jornal de Pediatria, 102, 101470.
https://doi.org/10.1016/j.jped.2025.101470
18. Dahò, M., & Caci, B. (2025). Clinical Decision Support Systems and Artificial Intelligence in ADHD Assessment and Rehabilitation: Opportunities and Challenges for Technology-Assisted Care. Healthcare, 13(23), 3171. https://doi.org/10.3390/healthcare13233171
19. Dai, Y. W., & Hsu, C. F. (2025). Machine Learning for ADHD Diagnosis: Feature Selection from Parent Reports, Self-Reports and Neuropsychological Measures. Children, 12(11), 1448.
https://doi.org/10.3390/children12111448
20. D’Aiello, B., Di Vara, S., De Rossi, P., Vicari, S., & Menghini, D. (2024). The effect of a single dose of methylphenidate on attention in children and adolescents with ADHD and comorbid oppositional defiant disorder. PLoS One, 19(8), e0299449. https://doi.org/10.1371/journal.pone.0299449
21. Demircioğlu, A. (2024). Applying oversampling before cross-validation will lead to high bias in radiomics. Scientific Reports, 14, 11563. https://doi.org/10.1038/s41598-024-62585-z
22. Duda, M., Haber, N., Daniels, J., & Wall, D. P. (2017). Crowdsourced validation of a machine-learning classification system for autism and ADHD. Translational Psychiatry, 7, e1133.
https://doi.org/10.1038/tp.2017.86
23. Ebadi, A., Allouch, S., Mheich, A., Tabbal, J., Kabbara, A., Robert, G., Lefebvre, A., Iftimovici, A., Rodríguez-Herreros, B., Chabane, N., & Hassan, M. (2025). Beyond homogeneity: Charting the landscape of heterogeneity in neurodevelopmental and psychiatric electroencephalography. Translational Psychiatry, 15(1), 223. https://doi.org/10.1038/s41398-025-03441-0
24. Eng, A. G., Bansal, P. S., Goh, P. K., Nirjar, U., Petersen, M. K., & Martel, M. M. (2024). Evidence-based assessment for attention-deficit/hyperactivity disorder. Assessment, 31(1), 42–52.
https://doi.org/10.1177/10731911221149957
25. Ghasemi, E., Ebrahimi, M., & Ebrahimie, E. (2022). Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials. Cognitive Neurodynamics, 16(6), 1335–1349. https://doi.org/10.1007/s11571-021-09746-2
26. Gholampour, S. (2024). Impact of nature of medical data on machine and deep learning for imbalanced datasets: clinical validity of SMOTE is questionable. Machine Learning and Knowledge Extraction, 6(2), 827–841.
https://doi.org/10.3390/make6020039
27. Ging-Jehli, N. R., & Pine, D. S. (2026). From symptom-based heterogeneity to mechanism-based profiling in youth ADHD: the promise of computational psychiatry. Neuropsychopharmacology, 51(1), 234–243.
https://doi.org/10.1038/s41386-025-02254-5
28. Gizaw, Z., Astale, T., & Kassie, G. M. (2022). What improves access to primary healthcare services in rural communities? A systematic review. BMC Primary Care, 23(1), 313.
https://doi.org/10.1186/s12875-022-01919-0
29. Goh, P. K., Elkins, A. R., Bansal, P. S., Eng, A. G., & Martel, M. M. (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, 679–691. https://doi.org/10.1007/s10802-023-01022-7
30. Guo, J., Wang, M., & Guo, X. (2024). Hierarchical hyperedge graph transformer toward dynamic interactions of brain networks for neurodevelopmental disease diagnosis. IEEE Access, 12, 162145–162156.
https://doi.org/10.1109/ACCESS.2024.3468439
31. Hao, J., & Ho, T. K. (2019). Machine learning made easy: a review of scikit-learn package in python programming language. Journal of Educational and Behavioral Statistics, 44(3), 348-361.
https://doi.org/10.3102/1076998619832248
32. Hu, Y., Ran, J., Qiao, R., Xu, J., Tan, C., Hu, L., & Tian, Y. (2024). Identifying ADHD‐related abnormal functional connectivity with a graph convolutional neural network. Neural Plasticity, 2024(1), 8862647.
https://doi.org/10.1155/2024/8862647
33. Hua, C., Wang, X. L., & Sheng, H. (2026). Neurodevelopmental disorders in children: the role of MRI in early detection and intervention planning. Frontiers in Neuroscience, 20, 1758568.
https://doi.org/10.3389/fnins.2026.1758568
34. Huynh, N., Yan, D., Ma, Y., Wu, S., Long, C., Sami, M. T., Almudaifer, A., Jiang, Z., Chen, H., Dretsch, M. N., Denney, T. S., Deshpande, R., & Deshpande, G. (2024). The use of generative adversarial network and graph convolution network for neuroimaging-based diagnostic classification. Brain Sciences, 14(5), 456.
https://doi.org/10.3390/brainsci14050456
35. Johnson, S., Lim, E., Jacoby, P., Faraone, S. V., Su, B. M., Solmi, M., Forrest, B., Furfaro, B., von Klier, K., Downs, J., & Chen, W. (2026). Prevalence of attention deficit hyperactivity disorder/hyperkinetic disorder of pediatric and adult populations in clinical settings: A systematic review, meta-analysis and meta-regression. Molecular Psychiatry, 31(1), 576–586. https://doi.org/10.1038/s41380-025-03178-8
36. Kamphaus, R. W., & Reynolds, C. R. (2015). Behavior Assessment System for Children (3rd ed.). Pearson.
37. Karabiber Cura, O., Kocaaslan Atli, S., & Akan, A. (2023). Attention deficit hyperactivity disorder recognition based on intrinsic time-scale decomposition of EEG signals. Biomedical Signal Processing and Control, 81, 1–11. https://doi.org/10.1016/j.bspc.2022.104512
38. Karteczka-Świętek, K., Opozda-Suder, S., & Strojny, A. (2022). Psychosocial functioning of adolescents with ADHD in the family, school and peer group: A scoping review protocol. Plos One, 17(6), e0269495.
https://doi.org/10.1371/journal.pone.0269495
39. Khushi, M., Shaukat, K., Alam, T. M., Hameed, I. A., Uddin, S., Luo, S., Yang, X., & Reyes, M. C. (2021). A comparative performance analysis of data resampling methods on imbalance medical data. IEEE Access, 9, 109960–109975. https://doi.org/10.1109/ACCESS.2021.3102399
40. Kim, W. P., Kim, H. J., Pack, S. P., Lim, J. H., Cho, C. H., & Lee, H. J. (2023). Machine Learning-Based Prediction of Attention-Deficit/Hyperactivity Disorder and Sleep Problems with Wearable Data in Children. JAMA Network Open, 6(3), e233502. https://doi.org/10.1001/jamanetworkopen.2023.3502
41. Knyazhansky, M., & Shrot, T. (2025). ADHD diagnostic tools across ages: traditional and digital approaches. Frontiers in Psychiatry, 16, 1668070. https://doi.org/10.3389/fpsyt.2025.1668070
42. Li, Y., Zeng, W., Dong, W., Cai, L., Wang, L., Chen, H., Yan, H., Bian, L., & Wang, N. (2025). MHNet: Multi-view High-Order Network for Diagnosing Neurodevelopmental Disorders Using Resting-State fMRI. Journal of Imaging Informatics in Medicine, 38, 2994–3014. https://doi.org/10.1007/s10278-025-01399-5
43. Lin, I. C., Chang, S. C., Huang, Y. J., Kuo, T. B. J., & Chiu, H. W. (2023). Distinguishing different types of attention deficit hyperactivity disorder in children using artificial neural network with clinical intelligent test. Frontiers in Psychology, 13, 1067771. https://doi.org/10.3389/fpsyg.2022.1067771
44. Lohani, D. C., & Rana, B. (2023). ADHD diagnosis using structural brain MRI and personal characteristic data with machine learning framework. Psychiatry Research: Neuroimaging, 334, 111689.
https://doi.org/10.1016/j.pscychresns.2023.111689
45. López, C. Q., Vera, V. D., & Quintero, M. J. (2025). Diagnosis of ADHD in Children with EEG and Machine Learning: Systematic Review and Meta-analysis. Clinical and Health, 36(2), 109–121.
https://doi.org/10.5093/clh2025a16
46. Madububambachu, U., Ukpebor, A., & Ihezue, U. (2024). Machine learning techniques to predict mental health diagnoses: A systematic literature review. Clinical Practice and Epidemiology in Mental Health: CP & EMH, 20, e17450179315688. https://doi.org/10.2174/0117450179315688240607052117
47. Mahjoob, M., Cardy, R., Penner, M., Anagnostou, E., Andrade, B. F., Crosbie, J., Kelley, E., Ayub, M., Brian, J., Iaboni, A., Schachar, R., Georgiades, S., Nicolson, R., Jones, J., & Kushki, A. (2024). Predictors of health-related quality of life for children with neurodevelopmental conditions. Scientific Reports, 14(1), 6377.
https://doi.org/10.1038/s41598-024-56821-9
48. Maniruzzaman, M., Shin, J., Hasan, M. A. M., & Yasumura, A. (2022). Efficient Feature Selection and Machine Learning Based ADHD Detection Using EEG Signal. Computers, Materials & Continua, 72(3), 5179–5195. https://doi.org/10.32604/cmc.2022.028339
49. McWhirter, A. C., & Walters, K. P. (2024). The Development of Ecologically Integrated and Culturally Informed ADHD and SLD Diagnostic Tools for Doctoral Assessment Training. Disabilities, 4(3), 583–615.
https://doi.org/10.3390/disabilities4030037
50. Mehrzad, F., Tehrani-Doost, M., Fallahinia, N., Shahrivar, Z., & Mahmoudi Gharaei, J. (2026). Psychometric properties of the Conners’ 3-parent rating scale in a clinical sample of Iranian children. BMC Psychiatry, 26, 468. https://doi.org/10.1186/s12888-026-07928-y
51. Mikolas, P., Vahid, A., Bernardoni, F., Süß, M., Martini, J., Beste, C., & Bluschke, A. (2022). Training a machine learning classifier to identify ADHD based on real-world clinical data from medical records. Scientific Reports, 12, 12934. https://doi.org/10.1038/s41598-022-17126-x
52. Miller, L. E., Kaseda, E. T., Koop, J. I., Mau, K. A., & Heffelfinger, A. K. (2021). Differential access to neuropsychological evaluation in children with perinatal complications or autism spectrum disorder: Impact of sociodemographic factors. The Clinical Neuropsychologist, 35(5), 988–1008.
https://doi.org/10.1080/13854046.2020.1837247
53. Morris, S. S., Timmons, A., & Musser, E. D. (2023). An individualized, Data-Driven biological approach to Attention-Deficit/Hyperactivity disorder (ADHD) heterogeneity. Research on Child and Adolescent Psychopathology, 51(11), 1565–1579. https://doi.org/10.1007/s10802-023-01104-6
54. Musullulu, H. (2025). Evaluating attention deficit and hyperactivity disorder (ADHD): a review of current methods and issues. Frontiers in Psychology, 16, 1466088. https://doi.org/10.3389/fpsyg.2025.1466088
55. Narzisi, A., Barbetti, F., Fabbri-Destro, M., Berloffa, S., Fantozzi, P., Viglione, V., Muccio, R., Valente, E., Accorinti, I., Foti, E., Milone, A., Cardillo, R., & Masi, G. (2026). Cognitive and emotional profiles in children with ASD, ADHD and comorbid presentations: evidence for a distinct clinical phenotype. Frontiers in Psychiatry, 17, 1765698. https://doi.org/10.3389/fpsyt.2026.1765698
56. Nazmin, F., Roy, A., Bushra, T., Retina, I. J., Arnab, K. H., & Ferdaus, F. (2024). Exploring the Prevalence and Social Determinants of ADHD and Comorbidities Among Urban School Aged Children in Bangladesh. Asia Pacific Journal of Medical Innovations, 1(2), 61–67.
https://doi.org/10.70818/apjmi.2024.v01i02.010
57. Nguyen, V., Montout, C., Mura, T., Purper-Ouakil, D., & Lopez-Castroman, J. (2024). Concordance and validity between versions of the ADHD Conners scale for parents. L’Encéphale, 50(4), 373–379.
https://doi.org/10.1016/j.encep.2023.07.006
58. Nigg, J. T. (2026). The evolving ADHD phenotype in the externalizing context. American Journal of Psychiatry, 183(1), 18–30. https://doi.org/10.1176/appi.ajp.20251165
59. Niina, A., Eyre, O., Wootton, R., Stergiakouli, E., Thapar, A., & Riglin, L. (2022). Exploring ADHD Symptoms and Associated Impairment across Development. Journal of Attention Disorders, 26(6), 822–830.
https://doi.org/10.1177/10870547211025612
60. Öztekin, I., Finlayson, M. A., Graziano, P. A., & Dick, A. S. (2021). Is there any incremental benefit to conducting neuroimaging and neurocognitive assessments in the diagnosis of ADHD in young children? A machine learning investigation. Developmental Cognitive Neuroscience, 49, 100966.
https://doi.org/10.1016/j.dcn.2021.100966
61. Polanczyk, G. V., Willcutt, E. G., Salum, G. A., Kieling, C., & Rohde, L. A. (2014). ADHD prevalence estimates across three decades: an updated systematic review and meta-regression analysis. International Journal of Epidemiology, 43(2), 434–442. https://doi.org/10.1093/ije/dyt261
62. Portellano Pérez, J. A., & Martínez Arias, R. (2020). TFV: Test de Fluidez Verbal: Para la evaluación del lenguaje y de las funciones ejecutivas. TEA Ediciones.
63. Python Software Foundation. (2026). Python (Version 3.12.13) [Software]. https://www.python.org/
64. Qin, H., Zhang, L., Wang, J., Yan, W., Wang, X., Qu, X., Peng, N., & Wang, L. (2025). Interpretable machine learning approaches for children’s ADHD detection using clinical assessment data: An online web application deployment. BMC Psychiatry, 25, 139. https://doi.org/10.1186/s12888-025-06573-1
65. Quintero López, C., Gil Vera, V. D., Cerpa Bernal, R. M., & Herrera Martínez, M. (2024). Prediction of ADHD with machine learning: A systematic literature review. Revista Argentina de Ciencias del Comportamiento, 16(3), 14–32. https://doi.org/10.32348/1852.4206.v16.n3.42221
66. Quintero-López, C., Gil-Vera, V. D., Landinez-Martínez, D. A., Vargas-Gaviria, J. P., & Gómez-Muñoz, N. (2023). Predictive Neurocognitive Model of Attention Deficit Hyperactivity Disorder Diagnosis. Mediterranean Journal of Clinical Psychology, 11(1). https://doi.org/10.13129/2282-1619/mjcp-3606
67. Quintero-López, C., Gil-Vera, V. D., Valencia Ochoa, J., & Herrera Velez, A. (2024). The Effect of Working Memory on Social Skills in Male Children with ADHD: A Structural Equation Model. Mediterranean Journal of Clinical Psychology, 12(2). https://doi.org/10.13129/2282-1619/mjcp-4130
68. Qureshi, M. N. I., Oh, J., Min, B., Jo, H. J., & Lee, B. (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. Frontiers in Human Neuroscience, 11, 157.
https://doi.org/10.3389/fnhum.2017.00157
69. Rajput, D., Wang, W. J., & Chen, C. C. (2023). Evaluation of a decided sample size in machine learning applications. BMC bioinformatics, 24(1), 48. https://doi.org/10.1186/s12859-023-05156-9
70. Rodríguez-Prieto, P., Giral-Oliveros, N. A., Simpson, I. C., & Ibáñez-Alfonso, J. A. (2024). Cognitive stimulation in socioeconomically disadvantaged children with neurodevelopmental disorders: a case series. Frontiers in Psychology, 15, 1365697. https://doi.org/10.3389/fpsyg.2024.1365697
71. Rosenthal, E. A., Broos, H. C., Timpano, K. R., & Johnson, S. L. (2024). Does emotion-related impulsivity relate to specific ADHD symptom dimensions and do the effects generalize across comorbid internalizing and externalizing syndromes? Journal of Attention Disorders, 28(2), 178–188.
https://doi.org/10.1177/10870547231210283
72. Salari, N., Ghasemi, H., Abdoli, N., Rahmani, A., Shiri, M. H., Hashemian, A. H., Akbari, H., & Mohammadi, M. (2023). The global prevalence of ADHD in children and adolescents: A systematic review and meta-analysis. Italian Journal of Pediatrics, 49(1), 48. https://doi.org/10.1186/s13052-023-01456-1
73. Sanchis, J., García-Ponsoda, S., Teruel, M. A., Trujillo, J., & Song, I. Y. (2024). A novel approach to identify the brain regions that best classify ADHD by means of EEG and deep learning. Heliyon, 10(4), e26028. https://doi.org/10.1016/j.heliyon.2024.e26028
74. Santos, C. S., & Amorim-Lopes, M. (2025). Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review. BMC Medical Research Methodology, 25(1), 45. https://doi.org/10.1186/s12874-025-02463-y
75. Schmengler, H., Peeters, M., Stevens, G. W., Hartman, C. A., Oldehinkel, A. J., & Vollebergh, W. A. (2023). ADHD symptoms and educational level in adolescents: The role of the family, teachers and peers. Research on Child and Adolescent Psychopathology, 51(7), 1051–1066. https://doi.org/10.1007/s10802-023-01047-y
76. Schmidt, E. M., Rothenberg, W. A., Davidson, B. C., Barnett, M., Jent, J., Cadenas, H., Fernandez, C., & Davis, E. (2023). Psychometric properties of the behavior assessment system for children student observation system (BASC-3 SOS) with young children in special education. Journal of Behavioral Education, 32(3), 543–564. https://doi.org/10.1007/s10864-021-09458-x
77. Sethurao, A., Amit, H. S., Vaishnavi, M. V., Balachandra, A., & Manoj Kumar, M. V. (2021). Classification of attention deficit hyperactivity disorder using machine learning. 2021 International Conference on Forensics, Analytics, Big Data and Security (FABS). https://doi.org/10.1109/FABS52071.2021.9702614
78. Slobodin, O., Yahav, I., & Berger, I. (2020). A Machine-Based Prediction Model of ADHD Using CPT Data. Frontiers in Human Neuroscience, 14, 560021. https://doi.org/10.3389/fnhum.2020.560021
79. Song, C., Jiang, Z. Q., Liu, D., & Wu, L. L. (2022). Application and research progress of machine learning in the diagnosis and treatment of neurodevelopmental disorders in children. Frontiers in Psychiatry, 13, 960672.
https://doi.org/10.3389/fpsyt.2022.960672
80. Sonuga-Barke, E. J. S., Becker, S. P., Bölte, S., Castellanos, F. X., Franke, B., Newcorn, J. H., Nigg, J. T., Rohde, L. A., & Simonoff, E. (2023). Annual research review: Perspectives on progress in ADHD science from characterization to cause. Journal of Child Psychology and Psychiatry, 64(4), 506–532.
https://doi.org/10.1111/jcpp.13696
81. Stratton, S. J. (2023). Population sampling: Probability and non-probability techniques. Prehospital and Disaster Medicine, 38(2), 147-148. https://doi.org/10.1017/S1049023X23000304
82. Tian, L., Zheng, H., Zhang, K., Qiu, J., Song, X., Li, S., Zeng, Z., Ran, B., Deng, X., & Cai, J. (2024). Structural or/and functional MRI-based machine learning techniques for attention-deficit/hyperactivity disorder diagnosis: A systematic review and meta-analysis. Journal of Affective Disorders, 355, 459–469.
https://doi.org/10.1016/j.jad.2024.03.111
83. Watkins, M. W., & Canivez, G. L. (2021). Assessing the psychometric utility of IQ scores: A tutorial using the Wechsler Intelligence Scale for Children–Fifth Edition. School Psychology Review, 50(4), 577–596.
https://doi.org/10.1080/2372966X.2020.1816804
84. Watkins, M. W., Canivez, G. L., Dombrowski, S. C., McGill, R. J., Pritchard, A. E., Holingue, C. B., & Jacobson, L. A. (2022). Long-term stability of Wechsler Intelligence Scale for Children–fifth edition scores in a clinical sample. Applied Neuropsychology: Child, 11(3), 422–428.
https://doi.org/10.1080/21622965.2021.1875827
85. Wechsler, D. (2015). Wechsler Intelligence Scale for Children–Fifth Edition (WISC-V). Pearson.
86. Willig, T. N., Dajon, M., Assathiany, R., Brun, L., Fourneret, P., Massé, M., Monge, A., Piollet, A., Thiollier, M. C., Cortese, S., & Purper-Ouakil, D. (2024). Healthcare pathways and practitioners’ knowledge about ADHD in children. L’Encéphale, 50(4), 363–372. https://doi.org/10.1016/j.encep.2023.07.005
87. Wu, Z. M., Wang, P., Liu, L., Liu, J., Cao, X. L., Sun, L., Cao, Q. J., Yang, L., Wang, Y. F., & Yang, B.-R. (2022). ADHD-inattentive versus ADHD-combined subtypes: A severity continuum or two distinct entities? A comprehensive analysis of clinical, cognitive and neuroimaging data. Journal of Psychiatric Research, 149, 28–36. https://doi.org/10.1016/j.jpsychires.2022.02.012
88. Yasumura, A., Omori, M., Fukuda, A., Takahashi, J., Yasumura, Y., Nakagawa, E., Koike, T., Yamashita, Y., Miyajima, T., Koeda, T., Aihara, M., Tachimori, H., & Inagaki, M. (2020). Applied Machine Learning Method to Predict Children with ADHD Using Prefrontal Cortex Activity: A Multicenter Study in Japan. Journal of Attention Disorders, 24(14), 2012–2020. https://doi.org/10.1177/1087054717740632
89. Yen, C., Lin, C. L., & Chiang, M. C. (2023). Exploring the frontiers of neuroimaging: a review of recent advances in understanding brain functioning and disorders. Life, 13(7), 1472.
https://doi.org/10.3390/life13071472
90. Yue, X., Liu, L., Chen, W., Preece, D. A., Liu, Q., Li, H., Wang, Y., & Qian, Q. (2022). Affective-cognitive-behavioral heterogeneity of Attention-Deficit/Hyperactivity Disorder (ADHD): Emotional dysregulation as a sentinel symptom differentiating “ADHD-simplex” and “ADHD-complex” syndromes? Journal of Affective Disorders, 307, 133–141. https://doi.org/10.1016/j.jad.2022.03.065
91. Zaheer, A., & Akhtar, A. (2025). Artificial intelligence as a support to diagnose ADHD: an insight of unorthodox approaches: a scoping review. Child Neuropsychology, 31(8), 1324–1358.
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