ABSTRACT
One of the primary psychiatric disorders is Autistic Spectrum Disorder (ASD). ASD is a mental disorder that limits the use of linguistic, communicative, cognitive, skills as well as social skills and abilities. Recently, ASD has been studied in the behavioural sciences using intelligent methods based around machine learning to speed up the screening time or to improve sensitivity, specificity or accuracy of the diagnosis process. Machine learning considers the ASD diagnosis problem as a classification task in which predictive models are built based on historical cases and controls. These models are supposed to be plugged into a screening tool to accomplish one or more of the aforementioned goals. In this paper, we shed light on recent studies that employ machine learning in ASD classification in order to discuss their pros and cons. Moreover, we highlight a noticeable problem associated with current ASD screening tools; the reliability of these tools using the DSM-IV rather than the DSM-5 manual. Hence the necessity to amend current screening tools to reflect the new imposed criteria of ASD classification in the DSM-5 particularly the diagnostic algorithms embedded within these methods.
- Abdelhamid N. and Thabtah F. 2014. Associative Classification Approaches: Review and Comparison. Journal of Information and Knowledge Management (JIKM), 13(3). incorrectGoogle ScholarCross Ref
- Allison C, Auyeung B, Baron-Cohen S. 2012. Toward brief "Red Flags" for autism screening: the short Autism Spectrum Quotient and the short quantitative checklist for autism in toddlers in 1,000 cases and 3,000 controls (2012) J Am Acad Child Adolesc Psychiatry 2012, 51(2):202--12Y. Google ScholarCross Ref
- American Psychiatric Association. 2013. Diagnostic and statistical manual of mental disorders: DSM-5. Washington, D.C: American Psychiatric Association.Google Scholar
- American Psychiatric Association 2000. Diagnostic and statistical manual for mental disorder (4th edn), Text revision. Washington, DC: American Psychiatric Association.Google Scholar
- Bennett M., Goodall E. 2016. A Meta-analysis of DSM-5 Autism Diagnoses in Relation to DSM-IV and DSM-IV-TR. Review Journal of Autism and Developmental Disorders. June 2016, Volume 3, Issue 2, pp 119--124 Google ScholarCross Ref
- Berument, S. K., Rutter, M., Lord, C., Pickles, A., Bailey, A., MRC Child Psychiatry Unit, & Social, Genetic and Developmental Psychiatry Research Centre 2000. Autism Screening Questionnaire: Diagnostic validity. British Journal of Psychiatry, 175, 444--451.Google ScholarCross Ref
- Bolton P, Macdonald H, Pickles A, Rios P, Goode S, Crowson M, Bailey A, Rutter M. 1994. A case-control family history study of autism. J Child Psychol Psychiatr 35:877--900. Google ScholarCross Ref
- Bone, D., Goodwin, M. S., Black, M. P., Lee, C.-C., Audhkhasi, K., & Narayanan, S. 2014. Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises. Journal of Autism and Developmental Disorders, 1--16. doi: 10.1007/s10803-014-2268-6 Google ScholarCross Ref
- Duda M., Ma R., Haber N., Wall D.P. 2016. Use of machine learning for behavioral distinction of autism and ADHD. Translational Psychiatry 9(6), 732. Google ScholarCross Ref
- Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I. (2009). The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1). Google ScholarDigital Library
- Geschwind DH. 2001. Sharing gene expression data: an array of options Nat Rev Neurosci. 2001 Jun;2(6):435-8. Google ScholarCross Ref
- Grzadzinski R, Huerta M, Lord C. 2013. DSM-5 and autism spectrum disorders (ASDs): an opportunity for identifying ASD subtypes. Mol Autism. 2013;4(1):12 Google ScholarCross Ref
- Kent RG1, Carrington SJ, Le Couteur A, Gould J, Wing L, Maljaars J, Noens I, van Berckelaer-Onnes I, Leekam SR 2013. Diagnosing Autism Spectrum Disorder: Who Will Get Dsm-5 Diagnosis. J Child Psychol Psychiatry. 2013 Nov;54(11):1242--50.Google Scholar
- Lord, C, Risi, S, Lambrecht, L et al, 2000. The Autism Diagnostic Observation Schedule-Generic: a standard measure of social and communication deficits associated with the spectrum of autism J Autism Dev Disord. 2000;30:205--223.7.Google Scholar
- Lord, C., Rutter, M., & Le Couteur, A. 1994. Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24, 659--685. Google ScholarCross Ref
- Mateescu L., Mihailescu I., Frunza A. A., Coman M., Rad F., Anghel C., Dobrescu L., Manea M. 2015. Assessment Tools For Autism Spectrum Disorders In Adults Patients. ANALELE UNIVERSITĂŢII "DUNĂREA DE JOS" DIN GALAŢI MEDICINĂ FASCICULA XVII, no. 1, 2015.Google Scholar
- Matson, J.L., Kozlowski, A.M., Hattier, M.A., Hotovitz, M., & Sipes, M. 2012. DSM-IV vs DSM-5 diagnostic criteria for toddlers with autism. Developmental Neurorehabilitation, 15, 185--190. Google ScholarCross Ref
- Mayer JL, Heaton PF (2014) Age and sensory processing abnormalities predict declines in encoding and recall of temporally manipulated speech in high-functioning adults with ASD Autism Res. 2014;7:40--9. Google ScholarCross Ref
- McPartland, J.C., Reichow, B., & Volkmar, F.R. 2012. Sensitivity and specificity of proposed DSM-5 diagnostic criteria for autism spectrum disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 51, 368--383. Google ScholarCross Ref
- Mohammad R., Thabtah F., McCluskey L., 2014. Predicting Phishing Websites based on Self-Structuring Neural Network. Journal of Neural Computing and Applications, (3)1--16. Springer.Google ScholarDigital Library
- Platt J. 1998. Fast training of SVM using sequential optimization, (Advances in kernel methods -- support vector learning, B. Scholkopf, C. Burges, A. Smola eds), MIT Press, Cambridge, 1998, pp. 185--208Google Scholar
- Quinlan J. 1993. C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann.Google Scholar
- Ruzich, E., Allison, C., Smith, P., Watson, P., Auyeung, B., Ring, H., & Baron-Cohen, S. 2015. Measuring autistic traits in the general population: a systematic review of the Autism-Spectrum Quotient (AQ) in a nonclinical population sample of 6,900 typical adult males and females. Molecular Autism, 6, 2. Google ScholarCross Ref
- Thabtah F. 2007. Review on Associative Classification Mining. Journal of Knowledge Engineering Review, Vol.22:1, 37--65. Cambridge Press. Google ScholarDigital Library
- Wall DP, Kosmiscki J, Deluca TF, Harstad L, Fusaro VA 2012. Use Of Machine Learning To Shorten Observation-Based Screening And Diagnosis Of Autism. Translational Psychiatry (2). Google ScholarCross Ref
- Wall DP, Dally R. Luyster R., Jung JY, Deluca TF. 2012. Use Of Artificial Intelligence To Shorten the behavioural diagnosis of autism. PlOs One 2012; 7:e43855. Google ScholarCross Ref
- Wing L, Leekam SR, Libby SJ, Gould J, Larcombe M. 2002. The Diagnostic Interview for Social and Communication Disorders: Background, inter-rater reliability and clinical use. Journal of Child Psychology and Psychiatry. 2002;43:307--32 Google ScholarCross Ref
- Wiggins LD, Reynolds A, Rice CE, Moody EJ, Bernal P, Blaskey L, Rosenberg SA, Lee LC, Levy SE. 2014. Using standardized diagnostic instruments to classify children with autism in the Study to Explore Early Development. Journal of Autism and Developmental Disorders. 2014.Google Scholar
Index Terms
- Autism Spectrum Disorder Screening: Machine Learning Adaptation and DSM-5 Fulfillment
Recommendations
Feature Selection Technique for Autism Spectrum Disorder
CCEAI '21: Proceedings of the 5th International Conference on Control Engineering and Artificial IntelligenceAutism Spectrum Disorder (ASD) is a developmental disorder that restricts the development of behaviors, communication, and learning skills. People with ASD have difficulties in communicating and engaging with other people. Presently, a questionnaire ...
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