Skip to main content
SpringerLink
Log in

Toward Machine Learning-Based Psychological Assessment of Autism Spectrum Disorders in School and Community

  • Conference paper
  • First Online:
Proceedings of Trends in Electronics and Health Informatics

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 376))

Abstract

The sensory processing system of the human body is capable of collecting, developing, and integrating information through sensory organs. Sensory impairment has been discovered in children with autism spectrum disorder (ASD). People with ASD are susceptible to hyper/hypo-sensitivity that might cause changes in information management, affect cognitive impairment, and social reactions to everyday events. This article proposed a questionnaire based on ASD symptoms found in previous studies with 82 questions. Following that, a dataset is created by conducting a survey using the questionnaire. Several machine learning models that can identify ASD and its types are also compared. Among the machine learning models, the artificial neural network achieved an accuracy of 89.8%. Implicit measurements and ecologically sound settings have shown excellent precision in predicting outcomes and the correct classification of populations into categories.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Data & statistics on autism spectrum disorder (2020). https://www.cdc.gov/ncbddd/autism/data.html

  2. Association AP et al (2012) Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Publications

    Google Scholar 

  3. Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, Abbasi-Kangevari M, Abbastabar H, Abd-Allah F, Abdelalim A et al (2020) Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet 396(10258):1204–1222

    Article  Google Scholar 

  4. Lord C, Risi S, Lambrecht L, Cook EH, Leventhal BL, DiLavore PC, Pickles A, Rutter M (2000) The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disorders 30(3):205–223

    Article  Google Scholar 

  5. Biswas M, Kaiser MS, Mahmud M, Al Mamun S, Hossain M, Rahman MA et al (2021) An XAI based autism detection: The context behind the detection. In: International conference on brain informatics. Springer, pp 448–459

    Google Scholar 

  6. Ghosh T, Al Banna MH, Rahman MS, Kaiser MS, Mahmud M, Hosen AS, Cho GH (2021) Artificial intelligence and internet of things in screening and management of autism spectrum disorder. Sustain Cities Soc 74:103189

    Article  Google Scholar 

  7. Hyde KK, Novack MN, LaHaye N, Parlett-Pelleriti C, Anden R, Dixon DR, Linstead E (2019) Applications of supervised machine learning in autism spectrum disorder research: a review. Rev J Autism Dev Disorders 6(2):128–146

    Article  Google Scholar 

  8. Faras H, Al Ateeqi N, Tidmarsh L (2010) Autism spectrum disorders. Ann Saudi Med 30(4):295–300

    Article  Google Scholar 

  9. Zwaigenbaum L, Penner M (2018) Autism spectrum disorder: advances in diagnosis and evaluation. BMJ 361

    Google Scholar 

  10. Berument SK, Rutter M, Lord C, Pickles A, Bailey A (1999) Autism screening questionnaire: diagnostic validity. Br J Psychiatry 175(5):444–451

    Article  Google Scholar 

  11. Sadek ET, AbdElSabour Seada NA, Ghoniemy S (2020) Computer vision techniques for autism symptoms detection and recognition: a survey. Int J Intell Comput Inf Sci 20(2):89–111

    Google Scholar 

  12. Rahman M, Usman OL, Muniyandi RC, Sahran S, Mohamed S, Razak RA et al (2020) A review of machine learning methods of feature selection and classification for autism spectrum disorder. Brain Sci 10(12):949

    Article  Google Scholar 

  13. Raj S, Masood S (2020) Analysis and detection of autism spectrum disorder using machine learning techniques. Procedia Comput Sci 167:994–1004

    Article  Google Scholar 

  14. Thabtah F, Peebles D (2020) A new machine learning model based on induction of rules for autism detection. Health Inf J 26(1):264–286

    Article  Google Scholar 

  15. Omar KS, Mondal P, Khan NS, Rizvi MRK, Islam MN (2019) A machine learning approach to predict autism spectrum disorder. In: 2019 International conference on electrical, computer and communication engineering (ECCE). IEEE, pp 1–6

    Google Scholar 

  16. Hossain MD, Kabir MA, Anwar A, Islam MZ (2021) Detecting autism spectrum disorder using machine learning techniques. Health Inf Sci Syst 9(1):1–13

    Article  Google Scholar 

  17. Alam ME, Kaiser MS, Hossain MS, Andersson K (2018) An IoT-belief rule base smart system to assess autism. In: 2018 4th International conference on electrical engineering and information & communication technology (iCEEiCT). IEEE, pp 672–676

    Google Scholar 

  18. Al Banna MH, Ghosh T, Taher KA, Kaiser MS, Mahmud M (2020) A monitoring system for patients of autism spectrum disorder using artificial intelligence. In: International conference on brain informatics. Springer, pp 251–262

    Google Scholar 

  19. Sumi AI, Zohora MF, Mahjabeen M, Faria TJ, Mahmud M, Kaiser MS (2018) fASSERT: A fuzzy assistive system for children with autism using internet of things. In: International conference on brain informatics. Springer, pp 403–412

    Google Scholar 

  20. Seltzer MM, Krauss MW, Shattuck PT, Orsmond G, Swe A, Lord C (2003) The symptoms of autism spectrum disorders in adolescence and adulthood. J Autism Dev Disorders 33(6):565–581

    Article  Google Scholar 

  21. Baskin JH, Sperber M, Price BH (2006) Asperger syndrome revisited. Rev Neurol Dis 3(1):1–7

    Google Scholar 

  22. Mirkovic B, Gérardin P (2019) Asperger’s syndrome: what to consider? L’encephale 45(2):169–174

    Article  Google Scholar 

  23. Karabekiroglu K (2011) Pervasive developmental disorder-not otherwise specified: specifying and differentiating. In: Autism spectrum disorders: the role of genetics in diagnosis and treatment p. 17

    Google Scholar 

  24. Snow AV, Lecavalier L (2011) Comparing autism, PDD-NOS, and other developmental disabilities on parent-reported behavior problems: little evidence for ASD subtype validity. J Autism Dev Disorders 41(3):302–310

    Article  Google Scholar 

  25. Mehra C, Sil A, Hedderly T, Kyriakopoulos M, Lim M, Turnbull J, Happe F, Baird G, Absoud M (2019) Childhood disintegrative disorder and autism spectrum disorder: a systematic review. Dev Med Child Neurol 61(5):523–534

    Article  Google Scholar 

  26. Elia M, Ferri R, Musumeci SA, Del Gracco S, Bottitta M, Scuderi C, Miano G, Panerai S, Bertrand T, Grubar JC (2000) Sleep in subjects with autistic disorder: a neurophysiological and psychological study. Brain Dev 22(2):88–92

    Article  Google Scholar 

  27. Militerni R, Bravaccio C, Falco C, Fico C, Palermo MT (2002) Repetitive behaviors in autistic disorder. Eur Child Adolescent Psychiatry 11(5):210–218

    Article  Google Scholar 

  28. Hagberg B, Goutières F, Hanefeld F, Rett A, Wilson J (1985) Rett syndrome: criteria for inclusion and exclusion. Brain Dev 7(3):372–373

    Article  Google Scholar 

  29. Kyle SM, Vashi N, Justice MJ (2018) Rett syndrome: a neurological disorder with metabolic components. Open Biol 8(2):170216

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh

    Sabbir Ahmed, Md. Farhad Hossain, Silvia Binte Nur & M. Shamim Kaiser

  2. Applied Intelligence and Informatics (AII), Wazed Miah Science Research Centre (WMSRC), Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh

    M. Shamim Kaiser

  3. Department of Computer Science, Nottingham Trent University, Nottingham, NG1 4FQ, UK

    Mufti Mahmud

Authors
  1. Sabbir Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Md. Farhad Hossain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Silvia Binte Nur
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Shamim Kaiser
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mufti Mahmud
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  2. National Institute for Materials Science, Tsukuba, Japan

    Anirban Bandyopadhyay

  3. Amity University, Jaipur, India

    Kanad Ray

  4. Pranveer Singh Institute Of Technology, Kanpur, India

    Raghvendra Singh

  5. Pranveer Singh Institute Of Technology, Kanpur, India

    Vishal Nagar

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ahmed, S., Hossain, M.F., Nur, S.B., Shamim Kaiser, M., Mahmud, M. (2022). Toward Machine Learning-Based Psychological Assessment of Autism Spectrum Disorders in School and Community. In: Kaiser, M.S., Bandyopadhyay, A., Ray, K., Singh, R., Nagar, V. (eds) Proceedings of Trends in Electronics and Health Informatics. Lecture Notes in Networks and Systems, vol 376. Springer, Singapore. https://doi.org/10.1007/978-981-16-8826-3_13

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-8826-3_13

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8825-6

  • Online ISBN: 978-981-16-8826-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation