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Prediction of Autism Spectrum Disorder Using Machine Learning

G. Shivakiran Sastry, K. Sanjay, Sriram Manideep, Srividhya Ganesan

Abstract


Autism Spectrum Disorder (ASD) is one of the neurocognitive disorders which is categorized by persistent difficulties since babyhood. The difficulties faced by an ASD individual commonly include less interaction with others, no response to the actions, reciprocal behaviors, hyperactive, etc. The research focused on the paper is concise to classification of an autistic children.


Keywords


Autism, Convolution Neural Network, Haar Cascade Classifier, Support Vector Machine, VGG16.

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References


Srividhya Ganesan, Sustainable Development of Green Healthcare Communities for Prediction of Autism Spectrum Disorder using Machine learning approach, Journal of Green Engineering, Scopus Indexed Journal, Volume-10, Issue -9, September 2020.

Srividhya Ganesan, Sustainable Development of Green Healthcare Communities for Prediction of Autism Spectrum Disorder using Machine learning approach, Journal of Psychology and Education, January 2021

Kos Micki JA, Sochat V, Duda M, Wall DP. Attempting to find a minimal set of behaviours for autism detection through feature selection-based machine learning. Trans Psychiatry.2015;5(2): e514.

Riley M, Karl J, Chris T. A study of early stopping, assembling, and patch working for cascade correlation neural networks. IAENG Int J Applied Mathematics. 2010;40(4):307-16.

Allison C, Baron-Cohen S, Charman T, Wheelwright S, Richler J, Pasco G, et al. The QCHAT (quantitative checklist for autism in toddlers): A normally distributed quantitative measure of autistic traits at 18-24 months of age: preliminary report. J Autism Dev Disord.2008;38(8):1414-25

Duda M, Ma R, Haber N, Wall DP. Use of machine learning for behavioural distinction of autism and ADHD. Trans Psychiatry. 2016;6(2): e732.

Bone D, Goodwin MS, Black MP, Lee CC, Audhkhasi K, Narayanan S. Applying machine learning to facilitate autism diagnostics: Pitfalls and promises. J Autism Dev Disord. 2015;45(5):1121-36.

Yonekura A, Kawanaka H, Surya Prasath VB, et al.

Escudero J, Ifeachor E, Zajicek JP, Green C, Shearer J, Pearson S. Machine learning-based method for personalized and cost-effective detection of Alzheimer’s disease. IEEE Trans Biomed Eng 2013; 60(1): 164-8. [http://dx.doi.org/10.1109/TBME.2012.2212278]

Liu S, Liu S, Cai W, et al. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans Biomed Eng 2015; 62(4): 1132-40. [http://dx.doi.org/10.1109/TBME.2014.2372011] [PMID: 25423647]

Abdar M, Zomorodi-Moghadam M, Zhou X, et al. A new nested ensemble technique for automated diagnosis of breast cancer. Pattern Recognit Lett [Internet] 2018.

Adem K, Kiliçarslan S, Cömert O. Classification and diagnosis of cervical cancer with softmax classification with stacked autoencoder. Expert Syst Appl 2019; 115: 557-64. [Internet]. [http://dx.doi.org/10.1016/j.eswa.2018.08.050]

Altman NS. An introduction to kernel and nearest- neighbor nonparametric regression. Am Stat 1992; 43(3): 175-85.

Dua D, Graff C. UCI Machine Learning Repository. Available at: http://archive.ics.uci.edu/mlIrvine.

Thabtah F. Autism Spectrum Disorder screening: Machine learning adaptation and DSM-5 fulfilment. ICMHI '17 Proceedings of the first International Conference on Medical and Health Informatics 2017; Taichung City, Taiwan May 20-22.

Thabtah F. ASDTests. A mobile app for ASD screening. [Internet]. 2017 [cited 2018 Dec 20]. Available from: ww.asdtests.com

Thabtah F. Machine learning in autistic spectrum disorder behavioral research: A review and ways forward. Informatics Heal. Soc. Care 2019; 44(3): 278-97.

Liu, B., Hsu, W., Ma, Y, ―Integrating classification and association rule mining‖, Proceedings of the fourth international conference on knowledge discovery and data mining, pp.80–86, 1998.

K. S. Omar, P. Mondal, N. S. Khan, M. R. K. Rizvi and M. N. Islam, "A Machine Learning Approach to Predict Autism Spectrum Disorder", International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox'sBazar, Bangladesh, pp. 1-6, 2019.

Alwidian J., Hammo B., and Obeid N, ―Enhanced CBA algorithm Based on Apriori Optimization‖, The 28th IBIMA conference on Vision 2020: Innovation Management, Development Sustainability, and Competitive Economic Growth, 2016.


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