Diagnosis of Attention Deficit Hyperactivity Disorder with Machine Learning Methods: A Systemic Review
PDF

REVIEW

Diagnosis of Attention Deficit Hyperactivity Disorder with Machine Learning Methods: A Systemic Review

Turk J Child Adolesc Ment Health 2024;31(3):179-185 DOI: 10.4274/tjcamh.galenos.2022.02486
, ,
PDF
Permissions

Abstract

Attention deficit hyperactivity disorder (ADHD) is a common neuro-developmental disorder in the community. The gold standard for diagnosis is clinician evaluation. In contrast, with the increase in technological developments, the number of studies on the use of machine learning (ML) in the diagnosis of this disorder is increasing. Studies have mainly focused on electroencephalogram and imaging methods. However, to our knowledge, no studies up to now integrated studies using various methods related to this issue. The purpose of this review was to provide information about the increasing body of literature and current studies on ADHD and ML in children and adolescents, summarize important studies, and offer suggestions for future studies on ML.

Keywords: Attention deficit, hyperactivity, machine learning, review

References

1

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. American Psychiatric Publishing, Arlington, VA 2013.

2

Polanczyk G, de Lima MS, Horta BL, Biederman J, Rohde LA. The worldwide prevalence of ADHD: a systematic review and metaregression analysis. Am J Psychiatry. 2007;164:942-98.

3

Ercan ES, Unsel-Bolat G, Tufan AE, Karakoc Demirkaya S, Bilac O, Celik G, Kılıç BG, Akyol Ardic U, Yalin Sapmaz S, Aksu H, Yolga Tahiroglu A, Karacetin G, Tural U, Aktepe E, Rodopman Arman A, Başgül S, Coşkun M, Dursun OB, Durukan İ, Perdahli Fiş N, Gençoğlan S, Gökçen C, Sarı Gokten E, Görker I, Görmez V, Yıldız Gündoğdu Ö, Hesapçioğlu ST, Kandemir H, Mutluer T, Nasiroğlu S, Özcan Ö, Şahin N, Toros F, Perçinel Yazici İ, Yazici KU, Yulaf Y, Yüksel T, Bilgic A, Altun H, Akdemir D, Mazlum B, Çakın Memik N, Foto Özdemir D, Üneri Ö, Ünal F. Effect of Impairment on the Prevalence and Comorbidities of Attention Deficit Hyperactivity Disorder in a National Survey: Nation-Wide Prevalence and Comorbidities of ADHD. J Atten Disord. 2022;26:674-684.

4

Shaw P, Sudre G. Adolescent Attention-Deficit/Hyperactivity Disorder: Understanding Teenage Symptom Trajectories. Biol Psychiatry. 2021;89:152-161.

5

Johnson J, Morris S, George S. Attention deficit hyperactivity disorder in adults: what the non-specialist needs to know. Br J Hosp Med (Lond). 2020;81:1-11.

6

Wong ICK, Banaschewski T, Buitelaar J, Cortese S, Döpfner M, Simonoff E, Coghill D; European ADHD Guidelines Group. Emerging challenges in pharmacotherapy research on attention-deficit hyperactivity disorder-outcome measures beyond symptom control and clinical trials. Lancet Psychiatry. 2019;6:528-537.

7

Mulraney M, Arrondo G, Musullulu H, Iturmendi-Sabater I, Cortese S, Westwood SJ, Donno F, Banaschewski T, Simonoff E, Zuddas A, Döpfner M, Hinshaw SP, Coghill D. Systematic Review and Meta-analysis: Screening Tools for Attention-Deficit/Hyperactivity Disorder in Children and Adolescents. J Am Acad Child Adolesc Psychiatry. 2022;61:982-996.

8

Faraone SV, Asherson P, Banaschewski T, Biederman J, Buitelaar JK, Ramos-Quiroga JA, Rohde LA, Sonuga-Barke EJ, Tannock R, Franke B. Attention-deficit/hyperactivity disorder. Nat Rev Dis Primers. 2015;1:15020.

9

Zhang Z, Li G, Xu Y, Tang X. Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review. Diagnostics (Basel). 2021;11:1402.

10

Kosinski M, Behrend TS. Editorial overview: Big data in the behavioral sciences. Curr Opin Behav Sci. 18, iv–vi.

11

Gillan C, Whelan R. What big data can do for treatment in psychiatry. Curr Opin Behav Sci. 2017;18:34-42.

12

Levy H, Levy BK. Diagnosis and effectiveness of monitoring attention deficit hyperactivity disorder. 2020. Accessed May 13, 2022. https://patents.google.com/patent/WO2020144575A1/en

13

Tai AMY, Albuquerque A, Carmona NE, Subramanieapillai M, Cha DS, Sheko M, Lee Y, Mansur R, McIntyre RS. Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry. Artif Intell Med. 2019;99:101704.

14

Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. eDoctor: machine learning and the future of medicine. J Intern Med. 2018;284:603-619.

15

Camargo G, Bugatti PH, Saito PTM. Active semi-supervised learning for biological data classification. PLoS One. 2020;15:e0237428.

16

Tor HT, Ooi CP, Lim-Ashworth NS, Wei JKE, Jahmunah V, Oh SL, Acharya UR, Fung DSS. Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals. Comput Methods Programs Biomed. 2021;200:105941.

17

Deshpande G, Wang P, Rangaprakash D, Wilamowski B. Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data. IEEE Trans Cybern. 2015;45:2668-2679.

18

Eslami T, Almuqhim F, Raiker JS, Saeed F. Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey. Front Neuroinform. 2021;14:575999.

19

Tosun M. Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning. Phys Eng Sci Med. 2021.

20

Parashar A, Kalra N, Singh J, Kumar Goyal R. Machine Learning Based Framework for Classification of Children with ADHD and Healthy Controls. Intell Autom Soft Comput. 2021;28:669-682.

21

Altınkaynak M, Dolu N, Güven A, Pektaş F, Özmen S, Demirci E, Izzetoğlu M. Diagnosis of Attention Deficit Hyperactivity Disorder with combined time and frequency features. Biocybern Biomed Eng. 2020;40:927-937.

22

Güven A, Altınkaynak M, Dolu N, Izzetoglu M, Pektaş F, Özmen S, Demirci E, Batbat T. Combining functional near-infrared spectroscopy and EEG measurements for the diagnosis of attention-deficit hyperactivity disorder. Neural Comput Appl. 2020;32.

23

Das W, Khanna S. A Robust Machine Learning Based Framework for the Automated Detection of ADHD Using Pupillometric Biomarkers and Time Series Analysis. Sci Rep. 2021;11:16370.

24

Zhang-James Y, Helminen EC, Liu J; ENIGMA-ADHD Working Group; Franke B, Hoogman M, Faraone SV. Evidence for similar structural brain anomalies in youth and adult attention-deficit/hyperactivity disorder: a machine learning analysis. Transl Psychiatry. 2021;11:82.

25

Liu S, Zhao L, Wang X, Xin Q, Zhao J, Guttery DS, Zhang YD. Deep Spatio-Temporal Representation and Ensemble Classification for Attention Deficit/Hyperactivity Disorder. IEEE Trans Neural Syst Rehabil Eng. 2021;29:1-10.

26

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

27

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. Applied Machine Learning Method to Predict Children With ADHD Using Prefrontal Cortex Activity: A Multicenter Study in Japan. J Atten Disord. 2020;24:2012-2020.

28

Yeh SC, Lin SY, Wu EH, Zhang KF, Xiu X, Rizzo A, Chung CR. A Virtual-Reality System Integrated With Neuro-Behavior Sensing for Attention-Deficit/Hyperactivity Disorder Intelligent Assessment. IEEE Trans Neural Syst Rehabil Eng. 2020;28:1899-1907.

29

Maniruzzaman M, Shin J, Hasan MAM. Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis. Appl Sci. 2022;12:2737.

30

Altun S, Alkan A, Altun H. Application of deep learning and classical machine learning methods in the diagnosis of attention deficit hyperactivity disorder according to temperament features. Concurr Comput Pract Exp. 2022;34

31

Aceves-Fernandez MA. Methodology proposal of ADHD classification of children based on cross recurrence plots. Nonlinear Dyn. 2021;104:1491-1505.

32

Chang Y, Stevenson C, Chen IC, Lin DS, Ko LW. Neurological state changes indicative of ADHD in children learned via EEG-based LSTM networks. J Neural Eng. 2022;19.

33

Zhao K, Duka B, Xie H, Oathes DJ, Calhoun V, Zhang Y. A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD. Neuroimage. 2022;246:118774.

34

Sidhu GS, Asgarian N, Greiner R, Brown MR. Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD. Front Syst Neurosci. 2012;6:74.

35

Zou L, Zheng J, Miao C, Mckeown MJ, Wang ZJ. 3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI. IEEE Access. 2017;5:23626-23636.

36

J Jung M, Tu Y, Park J, Jorgenson K, Lang C, Song W, Kong J. Surface-based shared and distinct resting functional connectivity in attention-deficit hyperactivity disorder and autism spectrum disorder. Br J Psychiatry. 2019;214:339-344.

37

Zhang-James Y, Chen Q, Kuja-Halkola R, Lichtenstein P, Larsson H, Faraone SV. Machine-Learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data. J Child Psychol Psychiatry. 2020;61:1370-1379.

38

Cervantes-Henríquez ML, Acosta-López JE, Martinez AF, Arcos-Burgos M, Puentes-Rozo PJ, Vélez JI. Machine Learning Prediction of ADHD Severity: Association and Linkage to ADGRL3, DRD4, and SNAP25. J Atten Disord. 2022;26:587-605.

39

Liu Y, Qu HQ, Mentch FD, Qu J, Chang X, Nguyen K, Tian X, Glessner J, Sleiman P, Hakonarson H. Application of deep learning algorithm on whole genome sequencing data uncovers structural variants associated with multiple mental disorders in African American patients. Mol Psychiatry. 2022;27:1469-1478.

40

Wang LJ, Kuo HC, Lee SY, Huang LH, Lin Y, Lin PH, Li SC. MicroRNAs serve as prediction and treatment-response biomarkers of attention-deficit/hyperactivity disorder and promote the differentiation of neuronal cells by repressing the apoptosis pathway. Transl Psychiatry. 2022;12:67.

41

Kim JW, Sharma V, Ryan ND. Predicting Methylphenidate Response in ADHD Using Machine Learning Approaches. Int J Neuropsychopharmacol. 2015;18:pyv052.

42

Uluyagmur-Ozturk M, Arman AR, Yilmaz SS, Findik OTP, Genc HE, Carkaxhiu-Bulut G. ADHD and ASD Classification Based on Emotion Recognition Data. IEEE. :810-813.