Machine Learning Approaches for Autism Spectrum Disorder Detection: A Systematic Review of Age-Specific Applications and Performance Metrics

Mrs. Pooja Amrish Patil; Dr. Jaydeep Patil; Dr. Sangram T. Patil

doi:10.32628/IJSRST25121174

Authors

Mrs. Pooja Amrish Patil Department of Computer Science, D. Y. Patil Agriculture and Technical University, Talsande, Maharashtra, India Author
Dr. Jaydeep Patil Department of Computer Science, D. Y. Patil Agriculture and Technical University, Talsande, Maharashtra, India Author
Dr. Sangram T. Patil Department of Computer Science, D. Y. Patil Agriculture and Technical University, Talsande, Maharashtra, India Author

DOI:

https://doi.org/10.32628/IJSRST25121174

Keywords:

Autism Spectrum Disorder, Machine Learning, Deep Learning,, Federated Learning, Healthcare Informatics

Abstract

Autism Spectrum Disorder is one of the biggest concerns in the healthcare sector, and it’s crucial to diagnose it at an early stage for patients with Autism Spectrum Disorder. This review focuses on the use of machine learning in diagnosing Autism Spectrum Disorder, drawing data from 100 papers between 2015 and 2024. We touched every possible method starting from the classic ones like Support Vector Machines (SVMs) to the new ones like federated learning. Proving the federated learning is actually great since it is very precise (up to 98%) while keeping people’s information personal, which is a crucial matter in the healthcare industry. But one cannot write-off the basic framework where people use standard machine learning models such as SVMs, which at this point achieve around 92% accuracy. Also, they are more convenient to be implemented in small clinics that do not possess many great computers, and etcetera. This review suggests that the most suitable ML approaches for Autism Spectrum Disorder detection need to consider accuracy, privacy and availability of resources. Lately, more developed technologies provide even better outcomes; nevertheless, conventional techniques provide terrific options for clinics without much complicated systems available. Thus, the study offers meaningful suggestions to facilitate the choice of the most suitable methods based on the comparison between these approaches. In sum, this review spans the existing gap between research advancements in state-of-art machine learning techniques and practical healthcare settings and provides important recommendations for enhancing Autism Spectrum Disorder screening across various contexts.

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References

American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders (DSM-5®). Arlington, VA: American Psychiatric Association. DOI: https://doi.org/10.1176/appi.books.9780890425596

Lai, M. C., Lombardo, M. V., & Baron-Cohen, S. (2014). Autism. The Lancet, 383(9920), 896-910. DOI: https://doi.org/10.1016/S0140-6736(13)61539-1

Liberati, A., et al. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions. BMJ, 339, b2700. DOI: https://doi.org/10.1136/bmj.b2700

Duda, M., Ma, R., Haber, N., & Wall, D. P. (2016). Use of machine learning for behavioral distinction of autism and ADHD. Translational Psychiatry, 6(2), e732. DOI: https://doi.org/10.1038/tp.2015.221

Heinsfeld, A. S., et al. (2018). Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage: Clinical, 17, 16-23. DOI: https://doi.org/10.1016/j.nicl.2017.08.017

Farooq, M. S., et al. (2023). Detection of autism spectrum disorder in children and adults using machine learning. Scientific Reports, 13, 9605. DOI: https://doi.org/10.1038/s41598-023-35910-1

Kong, Y., et al. (2019). Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier. Neurocomputing, 324, 63-68. DOI: https://doi.org/10.1016/j.neucom.2018.04.080

Wang, C., et al. (2020). Functional connectivity-based classification of autism and control using SVM-RFECV. Physics in Medicine, 65, 99-105. DOI: https://doi.org/10.1016/j.ejmp.2019.08.010

Bone, D., et al. (2016). Use of machine learning to improve autism screening and diagnostic instruments. Journal of Child Psychology and Psychiatry, 57(8), 927-937. DOI: https://doi.org/10.1111/jcpp.12559

Pagnozzi AM, Conti E, Calderoni S, Fripp J, Rose SE. A systematic review of structural MRI biomarkers in autism spectrum disorder: a machine learning perspective

Alexandre Abraham, Michael P Milham, Adriana Di Martino, R Cameron Crad-dock, Dimitris Samaras, Bertrand Thirion, and Gael Varoquaux. 2017. Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example. NeuroImage 147 (2017), 736–745.

Xia-an Bi, Yingchao Liu, Qin Jiang, Qing Shu, Qi Sun, and Jianhua Dai. 2018. The diagnosis of autism spectrum disorder based on the random neural network cluster. Frontiers in human neuroscience 12 (2018), 257.

Xia-an Bi, Yang Wang, Qing Shu, Qi Sun, and Qian Xu. 2018. Classification of autism spectrum disorder using random support vector machine cluster. Frontiers in genetics 9 (2018), 18.

Alexandre Abraham, Michael P Milham, Adriana Di Martino, R Cameron Crad-dock, Dimitris Samaras, Bertrand Thirion, and Gael Varoquaux. 2017. Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example. NeuroImage 147 (2017), 736–745. DOI: https://doi.org/10.1016/j.neuroimage.2016.10.045

Xia-an Bi, Yingchao Liu, Qin Jiang, Qing Shu, Qi Sun, and Jianhua Dai. 2018. The diagnosis of autism spectrum disorder based on the random neural network cluster. Frontiers in human neuroscience 12 (2018), 257. DOI: https://doi.org/10.3389/fnhum.2018.00257

Xia-an Bi, Yang Wang, Qing Shu, Qi Sun, and Qian Xu. 2018. Classification of autism spectrum disorder using random support vector machine cluster. Frontiers in genetics 9 (2018), 18 DOI: https://doi.org/10.3389/fgene.2018.00018

Xu Cao, Wenqian Ye, Elena Sizikova, Xue Bai, M. Coffee, H. Zeng, Jianguo Cao “Vitasd: Robust Vision Transformer Baselines for Autism Spectrum Disorder Facial Diagnosis” IEEE International Conference… 30 October 2022 Medicine, Computer Science DOI: https://doi.org/10.1109/ICASSP49357.2023.10094684

Sharabash, Shaimaa Fouad and Elghaish, Hany Ali (2024) "Early Autism Detection using Machine Learning Techniques: A Review," Journal of Engineering Research: Vol. 8: Iss. 4, Article 5.

Vaishnavi Sirigiri , Srilekha Katta “Detection of Autism Spectrum Disorder using Machine Learning: A Review” International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538

Hasan, S M & Uddin, Md Palash & Mamun, Md. Al & Sharif, Muhammad & Ulhaq, Anwaar. (2022). A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders. IEEE Access. PP. 1-1. 10.1109/ACCESS.2022.3232490.

Priyadarshini, I. Autism Screening in Toddlers and Adults Using Deep Learning and Fair AI Techniques. Future Internet 2023, 15, 292. https://doi.org/10.3390/fi15090292

C. Allison, B. Auyeung, and S. Baron-Cohen, ‘‘Toward brief ‘red flags’for autism screening: The short autism spectrum quotient and the short quantitative checklist in 1,000 cases and 3,000 controls,’’ J. Amer. Acad. Child Adolescent Psychiatry, vol. 51, no. 2, pp. 202–212, 2012. DOI: https://doi.org/10.1016/j.jaac.2011.11.003

Hasan SMM, Uddin MP, Mamun MA, Sharif MI, Ulhaq A, Krishnamoorthy G. A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders. IEEE Access. 2022; 10: 134561-134579.

Alhassan S, Soudani A, Almusallam M. Energy-efficient EEG-based scheme for autism spectrum disorder detection using wearable sensors. Sensors. 2023;23(4):2228. DOI: https://doi.org/10.3390/s23042228

Cao X, Cao J. Commentary: Machine learning for autism spectrum disorder diagnosis–challenges and opportunities–a commentary on Schulte-Rüther et al. (2022). J Child Psychol Psychiatry. 2023;64(7):966-967. DOI: https://doi.org/10.1111/jcpp.13764

Kuttala D, Mahapatra D, Subramanian R, Oruganti VRM. Dense attentive GAN-based one-class model for detection of autism and ADHD. J King Saud Univ-Comput Inf Sci. 2022;34(4):10444-10458 DOI: https://doi.org/10.1016/j.jksuci.2022.11.001

Rahman KK, Subashini MM. Identification of autism in children using static facial features and deep neural networks. Brain Sci. 2022;12(1):94 DOI: https://doi.org/10.3390/brainsci12010094

Priyadarshini, I. AutismScreening in Toddlers and AdultsUsing Deep Learning and Fair AITechniques. Future Internet 2023, 15,292. https://doi.org/10.3390/fi15090292 DOI: https://doi.org/10.3390/fi15090292

Zhu, F. et al. Multi-modal machine learning system in early screening for toddlers with autism spectrum disorders based on response to name. Front. Psychiatry 14, 34 (2023). DOI: https://doi.org/10.3389/fpsyt.2023.1039293

Elbattah, M., Carette, R., Cilia, F., Guérin, J. L. & Dequen, G. Applications of machine learning methods to assist the diagnosis of autism spectrum disorder. In Neural Engineering Techniques for Autism Spectrum Disorder, vol. 2, 99–119 (Academic Press, 2023). DOI: https://doi.org/10.1016/B978-0-12-824421-0.00013-8

Lawan, A. A., Cavus, N., Abdulrazak, U. I. & Tahir, S. Fundamentals of machine-learning modeling for behavioral screening and diagnosis of autism spectrum disorder. In Neural Engineering Techniques for Autism Spectrum Disorder, vol. 2, 253–268 (Academic Press 2023). DOI: https://doi.org/10.1016/B978-0-12-824421-0.00020-5

H.A. Helaly, M. Badawy, A.Y. Haikal, Deep learning approach for early detection of Alzheimer’s disease. Cogn. Comput. 14, 1711 (2021) DOI: https://doi.org/10.1007/s12559-021-09946-2

C. Kavitha, V. Mani, S.R. Srividhya, O.I. Khalaf, C.A.T. Romero, Early-stage Alzheimer’s disease prediction using machine learning models. Front. Public Health (2022). https:// doi. org/ 10. 3389/ fpubh. 2022. 853294 DOI: https://doi.org/10.3389/fpubh.2022.853294

T.M. Ghazal, G. Issa, Alzheimer disease detection empowered with transfer learning. Comput. Mater. Continua 70(3), 5005– 5019 (2022) DOI: https://doi.org/10.32604/cmc.2022.020866

R. Gaudiuso, E. Ewusi-Annan, W. Xia, N. Melikechi, Diagnosis of Alzheimer’s disease using laser-induced breakdown spec-troscopy and machine learning. Spectrochim. Acta Part B 171, 105931 (2020) DOI: https://doi.org/10.1016/j.sab.2020.105931

Gosling, C. J. et al. Efficacy of psychosocial interventions for Autism spectrum disorder: An umbrella review. Mol. Psychiatry 27,1–10 (2022). DOI: https://doi.org/10.1038/s41380-022-01670-z

Willsey, H. R., Willsey, A. J., Wang, B. & State, M. W. Genomics, convergent neuroscience and progress in understanding autism spectrum disorder. Nat. Rev. Neurosci. 23(6), 323–341 (2022). DOI: https://doi.org/10.1038/s41583-022-00576-7

Rahman, M. M. et al. A Review of machine learning methods of feature selection and classification for autism spectrum disorder

Liu, R. et al. Spatial–temporal co-attention learning for diagnosis of mental disorders from resting-state fMRI data. IEEE Trans. Neural Netw. Learn. Syst. https:// doi. org/ 10. 1109/ TNNLS. 2023. 32430 00 (2023).

Lord, C., Elsabbagh, M., Baird, G. & Veenstra-Vanderweele, J. Autism spectrum disorder. Lancet 392(10146), 508–520 (2018). 33. Bilic, P. et al. The liver tumor segmentation benchmark (lits). Med. Image Anal. 84, 102680 (2023) DOI: https://doi.org/10.1016/S0140-6736(18)31129-2

Husna, R. N. S., Syafeeza, A. R., Hamid, N. A., Wong, Y. C. & Raihan, R. A. Functional magnetic resonance imaging for autism

T. Akter, M. Shahriare Satu, M. I. Khan, M. H. Ali, S. Uddin, P. Lio, J. M. W. Quinn, and M. A. Moni, ‘‘Machine learning-based models for early stage detection of autism spectrum disorders,’’ IEEE Access, vol. 7, pp. 166509–166527, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2952609

Prof. D. C. Pardeshi, Parth S. Mishra, Shripad Kulkarni “Review Paper on An Early-Stage Autism SpectrumDetection System” DOI: 10.48175/IJARSCT-17224 ,Volume 4, Issue 3, April 2024 DOI: https://doi.org/10.48175/IJARSCT-17224

Al-Beltagi M, Saeed NK, Bediwy AS, Bediwy EA, Elbeltagi R. Decoding the genetic landscape of autism: A comprehensive review. World J Clin Pediatr 2024; 13(3): 98468 [PMID: 39350903 DOI: 10.5409/wjcp.v13.i3.98468] DOI: https://doi.org/10.5409/wjcp.v13.i3.98468

Baadel, Said & Thabtah, Fadi & Lu, Joan. (2020). A clustering approach for autistic trait classification. Informatics for Health and Social Care. 45. https://www.tandfonline.com/eprint/CFADPUPACJPTJZCV4MV7/full?target=10.1080/17538157.2019.1687482. 10.1080/17538157.2019.1687482.

Indra Devi K.B., Durai Raj Vincent P.M.,The emergence of artificial intelligence in autism spectrum disorder research: A review of neuro imaging and behavioral applications,Computer Science Review,Volume 56,2025, 100718,ISSN 1574-0137,https://doi.org/10.1016/j.cosrev.2024.100718. DOI: https://doi.org/10.1016/j.cosrev.2024.100718

S.Vinayagan , Ajila Jaya Reeta C., Shiny Malar .F.R,” Early-Stage Detection Of Autism Spectrum Disorder Using Machine Learning Framework’ 2024 JETIR June 2024, Volume 11, Issue 6