Detection of Autism Spectrum Disorder in Children Using Machine Learning Techniques

Al Banna MH, Ghosh T, Taher KA, Kaiser MS, Mahmud M. A monitoring system for patients of autism spectrum disorder using artificial intelligence. In: International conference on brain informatics. Cham: Springer; 2020. pp. 251–62.

Baron-Cohen S, Allen J, Gillberg C. Can autism be detected at 18 months? The needle, the haystack, and the CHAT. Br J Psychiatry. 1992;161:839–43.

Dataset: https://www.kaggle.com/fabdelja/autism-screening-for-toddlers. Accessed 1 Oct 2019.

Deshpande G, Libero LE, Sreenivasan KR, Deshpande HD, Kana RK. Identification of neural connectivity signatures of autism using machine learning. Front Hum Neurosci. 2013;7:670.

Duda M, Ma R, Haber N, Wall DP. Use of machine learning for behavioral distinction of autism and ADHD. Transl Psychiatry. 2016;6: e732.

Thabtah, F. An accessible and efficient autism screening method for behavioural data and predictive analyses. Health Informatics Journal 2019;25(4):1739–55. https://doi.org/10.1177/1460458218796636.

https://www.autism-society.org/what-is/facts-and-statistics/. Accessed 25 Dec 2019.

https://www.helpguide.org/articles/autism-learning-disabilities/autismspectrumdisorders.htm. Accessed 20 Dec 2019.

“KNN Classification using Scikit-learn”, https://www.datacamp.com/community/tutorials/k-nearest-neighbor-classification-scikit-learn. Accessed 8 Oct 2019.

Kosmicki JA, Sochat V, Duda M, Wall DP. Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning. Transl Psychiatry. 2015. https://doi.org/10.1038/tp.2015.7.

Li H, Parikh NA, He L. A novel transfer learning approach to enhance deep neural network classification of brain functional connectome. Front Neurosci. 2018. https://doi.org/10.3389/fnins.2018.00491.

Logistic Regression. https://medium.com/datadriveninvestor/logistic-regression-18afd48779ce. Accessed 7 Oct 2019.

Naive Bayes for Machine Learning. https://machinelearningmastery.com/naive-bayes-for-machine-learning/. Accessed 8 Oct 2019.

Parikh MN, Li H, He L. Enhancing diagnosis of autism with optimized machine learning models and personal characteristic data. Front Comput Neurosci. 2019. https://doi.org/10.3389/fncom.2019.00009.

Pratap A, Kanimozhiselvi C. Soft computing models for the predictive grading of childhood Autism—a comparative study. IJSCE. 2014;4:64–7.

Random Forests(r), Explained. https://www.kdnuggets.com/2017/10/random-forests-explained.html. Accessed 8 Oct 2019.

Sen B, Borle NC, Greiner R, Brown MR. A general prediction model for the detection of ADHD and Autism using structural and functional MRI. PLoS ONE. 2018;13: e0194856.

Support Vector Machine—Introduction to Machine Learning Algorithms. https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47. Accessed 7 Oct 2019.

Support vector machines: The linearly separable case, https://nlp.stanford.edu/IR-book/html/htmledition/support-vector-machines-the-linearly-separable-case-1.html. Accessed 8 Oct 2019.

Thabtah F. Machine learning in autistic spectrum disorder behavioral research: A review and ways forward. Inf Health Soc Care. 2017;44:278–97.

Thabtah F, Peebles D. A new machine learning model based on induction of rules for autism detection. Health Inform J. 2020;26(1):264–86.

Towle P, Patrick P. Autism spectrum disorder screening instruments for very young children: A systematic review. New York: Hindawi Publishing Corporation; 2016.

Vaishali R, Sasikala R. A machine learning based approach to classify Autism with optimum behaviour sets. Int J Eng Technol. 2017;7:18.

van den Bekerom B. Using machine learning for detection of autism spectrum disorder. In: 26th Twente Student Conference on IT, Feb 2017.