A machine learning autism classification based on logistic regression analysis
- 207 Downloads
Autistic Spectrum Disorder (ASD) is a neurodevelopmental condition associated with significant healthcare costs; early diagnosis could substantially reduce these. The economic impact of autism reveals an urgent need for the development of easily implemented and effective screening methods. Therefore, time-efficient ASD screening is imperative to help health professionals and to inform individuals whether they should pursue formal clinical diagnosis. Presently, very limited autism datasets associated with screening are available and most of them are genetic in nature. We propose new machine learning framework related to autism screening of adults and adolescents that contain vital features and perform predictive analysis using logistic regression to reveal important information related to autism screening. We also perform an in-depth feature analysis on the two datasets using information gain (IG) and Chi square testing (CHI) to determine the influential features that can be utilized in screening for autism. Results obtained reveal that machine learning technology was able to generate classification systems that have acceptable performance in terms of sensitivity, specificity and accuracy among others.
KeywordsAutism spectrum disorder Classification Clinical decision making Data mining Feature analysis Machine learning Sensitivity Specificity
- 1.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.Google Scholar
- 7.Bishop D. Definition, diagnosis & assessment in a history of autism by A. Feinstein. Chichester: Wiley-Blackwell; 2010.Google Scholar
- 9.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.Google Scholar
- 10.Constantino J. (SRS™) Social Responsiveness Scale. WPS, 2005. https://www.wpspublish.com/store/p/2993/srs-social-responsiveness-scale. Accessed 9 Dec 2018.
- 13.Garnett M, Attwood T. The Australian scale for Asperger syndrome. Australian National Autism Conference. Brisbane, Australia; 1995.Google Scholar
- 19.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.Google Scholar
- 22.Qabajeh I, Thabtah F, Chiclana F. Dynamic classification rules data mining method. J Manag Anal. 2015;2(3):233–53.Google Scholar
- 23.Quinlan J. Induction of decision trees. Mach Learn. 1986;1(1):81–106.Google Scholar
- 25.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.Google Scholar
- 26.Thabtah F. ASDTests. A mobile app for ASD screening. www.asdtests.com. Accessed November 30th, 2017.
- 27.Thabtah F. Machine learning in autistic spectrum disorder behavioral research: a review and ways forward. Inform Health Soc Care. 2018;43(2):1–20.Google Scholar
- 29.Thabtah. Detecting autistic traits using computational intelligence & machine learning techniques. Master of Research Thesis, School of Health, Department of Psychology, University of Huddersfield; 2019.Google Scholar
- 32.Towle P, Patrick P. Autism spectrum disorder screening instruments for very young children: a systematic review. Autism Res Treat. 2016;2016:4624829.Google Scholar