Abdelhamid N, Thabtah F, Abdel-jaber H. Phishing detection: A recent intelligent machine learning comparison based on models content and features. 2017 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 72–77. 2017/7/22, Beijing, China, 2017.
American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-5. Washington, D.C: American Psychiatric Association; 2013.
Allison C, Auyeung B, Baron-Cohen S. 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. J Am Acad Child Adolesc Psychiatr. 2012;51(2):202–17.
American Psychiatric Association (APA). Diagnostic and statistical manual of mental disorders. 5th ed. Arlington, VA: APA; 2013.
Auyeung BBC. The autism spectrum quotient: children’s version (aq-child). J Autism Dev Disord. 2008;38(7):1230–40.
Baron-Cohen S, Wheelwright S, Skinner R, Martin J, Clubley E. The autism-spectrum quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. Journal of Autism Development Disorder. 2001;31:5–17.
Bishop D. Definition, diagnosis & assessment in a history of autism by A. Feinstein. Chichester: Wiley-Blackwell; 2010.
Bone D, Bishop S, Black M, Goodwin M, Lord C, Narayanan S. Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion. J Child Psychol Psychiatry. 2016;57:927–37.
Bone D, Goodwin M, Black M, Lee C, Audhkhasi K, Narayanan S. Applying machine learning to facilitate autism diagnostics: pitfalls and promises. J Autism Dev Disord. 2014;45(5):1–16.
Constantino J. (SRS™) Social Responsiveness Scale. WPS, 2005. https://www.wpspublish.com/store/p/2993/srs-social-responsiveness-scale. Accessed 9 Dec 2018.
Duda M, Ma R, Haber N, Wall DP. Use of machine learning for behavioral distinction of autism and ADHD. Transl Psychiatr. 2016;9(6):732.
Fischbach G, Lord C. The Simons Simplex Collection: a resource for identification of autism genetic risk factors. Neuron. 2010;68:192–5.
Garnett M, Attwood T. The Australian scale for Asperger syndrome. Australian National Autism Conference. Brisbane, Australia; 1995.
Geschwind D, et al. The autism genetic resource exchange: a resource for the study of autism and related neuropsychiatric conditions. Am J Hum Genet. 2001;69:463–6.
Hall D, Huerta MF, McAuliffe MJ, Farber GK. Sharing heterogeneous data: the national database for autism research. Neuroinformatics. 2012;10:331–9.
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten I. The WEKA data mining software: an update. SIGKDD Explor. 2009;11(1):10–8.
Le Cessie S, van Houwelingen JC. Ridge estimators in logistic regression. Appl Stat. 1992;41(1):191–201.
Lord C, Rutter M, Le Couteur A. Autism diagnostic interview—revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord. 1994;24:659–85.
Liu H, Setiono R. Chi2: feature selection and discretization of numeric attribute. Proceedings of the Seventh IEEE International Conference on Tools with Artificial Intelligence, November 5-8, 1995, pp. 388.
Luo G. Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction. Health Inf Sci Syst. 2016;4(1):2.
Mohammad R, Thabtah F, McCluskey L. Intelligent rule-based phishing websites classification. IET Inf Secur. 2014;8(3):153–60.
Qabajeh I, Thabtah F, Chiclana F. Dynamic classification rules data mining method. J Manag Anal. 2015;2(3):233–53.
Quinlan J. Induction of decision trees. Mach Learn. 1986;1(1):81–106.
Robins D, Fein D, Barton M, Green J. The modified checklist for autism in toddlers: an initial study investigating the early detection of autism and pervasive developmental disorders. J Autism Dev Disord. 2001;31(2):131–44.
Thabtah F. Autism spectrum disorder screening: machine learning adaptation and DSM-5 fulfilment. Proceedings of the 1st International Conference on Medical and Health Informatics 2017, pp. 1–6. Taichung City, Taiwan, ACM; 2017.
Thabtah F. ASDTests. A mobile app for ASD screening. www.asdtests.com. Accessed November 30th, 2017.
Thabtah F. Machine learning in autistic spectrum disorder behavioral research: a review and ways forward. Inform Health Soc Care. 2018;43(2):1–20.
Thabtah F. An accessible and efficient autism screening method for behavioural data and predictive analyses. Health Inform J. 2018;19:1460458218796636. https://doi.org/10.1177/1460458218796636.
Thabtah. Detecting autistic traits using computational intelligence & machine learning techniques. Master of Research Thesis, School of Health, Department of Psychology, University of Huddersfield; 2019.
Thabtah F, Peebles D. A new machine learning model based on induction of rules for autism detection. Health Inform J. 2019. https://doi.org/10.1177/1460458218824711.
Thabtah F, Kamalov F, Rajab K. A new computational intelligence approach to detect autistic features for autism screening. Int J Med Inform. 2018;117:112–24.
Towle P, Patrick P. Autism spectrum disorder screening instruments for very young children: a systematic review. Autism Res Treat. 2016;2016:4624829.
Wall DP, Kosmiscki J, Deluca TF, Harstad L, Fusaro VA. Use of machine learning to shorten observation-based screening and diagnosis of autism. Transl Psychiatr. 2012;2(4):e100.
Wall DP, Dally R, Luyster R, Jung JY, Deluca TF. Use of artificial intelligence to shorten the behavioral diagnosis of autism. PLoS ONE. 2012;7(8):e43855.
Witten I, Frank E. Data mining: practical machine learning tools and techniques. Burlington: Morgan Kaufmann; 2005.