Abstract
The objective of the research is to foresee Autism Spectrum Disorder (ASD) in toddlers with the help of machine learning algorithms. Of late, machine learning algorithms play vital role to improve diagnostic timing and accuracy. This research work precisely compares and highlights the effectiveness of the feature selection algorithms viz. Chi Square, Recursive Feature Elimination (RFE), Correlation Feature Selection (CFS) Subset Evaluation, Information Gain, Bagged Tree Feature Selector and k Nearest Neighbor (kNN), and to improve the efficiency of Random Tree classification algorithm while modelling ASD prediction in toddlers. Analysis results uncover that the Random Tree dependent on highlights chosen by Extra Tree calculation beat the individual methodologies. The outcomes have been assessed utilizing the execution estimates, for example, Accuracy, Recall and Precision. We present the results and identify the attributes that contributed most in differentiating ASD in toddlers as per machine learning model used in this study.
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References
National Institute of Mental Health. https://www.nimh.nih.gov/health/topics/autism-spectrum-disorders-asd/index.shtml
Penner, M., Anagnostou, E., Ungar, W.J.: Practice patterns and determinants of wait time for autism spectrum disorder diagnosis in Canada. Mol. Autism 9, 16 (2018)
Thabtah, F., Kamalov, F., Rajab, K.: A new computational intelligence approach to detect autistic features for autism screening. Int. J. Med. Inform. 117, 112–124 (2018)
Tabtah, F.: Autism spectrum disorder screening: machine learning adaptation and DSM-5 fulfillment. In: Proceedings of the 1st International Conference on Medical and Health Informatics, pp. 1–6, Taichung City. ACM (2017)
Thabtah, F.: ASDTests a mobile app for ASD, screening (2017). www.asdtests.com
Thabtah, F.: Machine learning in autistic spectrum disorder behavioural research: a review. Inform. Health Soc. Care J. 44(3), 278–297 (2017)
Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.T.: Deep learning for healthcare: review, opportunities and challenges. Brief. Bioinf. 19(6), 1236–1246 (2018)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Academic Press, Cambridge (2012)
Breiman, L.: Classification and Regression Trees. Routledge, New York (1984)
Wall, D.P., Dally, R., Luyster, R., Jung, J.Y., Deluca, T.F.: Use of artificial intelligence to shorten the behavioural diagnosis of autism. PLoS One 7(8), e43855 (2012)
Wall, D.P., Kosmiscki, J., Deluca, T.F., Harstad, L., Fusaro, V.A.: Use of machine learning to shorten observation-based screening and diagnosis of Autism. Transl. Psychiatry. 2(2), e100 (2012)
Lopez Marcano, J.L.: Classification of ADHD and non-ADHD Using AR Models and Machine Learning Algorithms. (Doctoral dissertation), Virginia Tech (2016)
Duda, M., Ma, R., Haber, N., Wall, D.P.: Use of machine learning for behavioral distinction of autism and ADHD. Transl. Psychiatry. 9(6), 732 (2016)
Wolfers, T., Buitelaar, J.K., Beckmann, C.F., Franke, B., Marquand, A.F.: From estimating activation locality to predicting disorder: a review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neurosci. Biobehav. Rev. 57, 328–349 (2015)
Al-diabat, M.: Fuzzy data mining for autism classification of children. Int. J. Adv. Comput. Sci. Appl. 9(7), 11–17 (2018)
Pratap, A., Kanimozhiselvi, C.: Soft computing models for the predictive grading of childhood Autism—a comparative study. Int. J. Soft Comput. Eng. (IJSCE) 4(3), 64–67 (2014). ISSN 2231–2307
Pratap, A., Kanimozhiselvi, C.S., Vijayakumar, R., Pramod, K.V.: Predictive assessment of autism using unsupervised machine learning models. Int. J. Adv. Intell. Para. 6(2), 113–121 (2014). https://doi.org/10.1504/IJAIP.2014.062174
Maenner, M.J., Yeargin-Allsopp, M., Van Naarden Braun, K., Christensen, D.L., Schieve, L.A.: Development of a machine learning algorithm for the surveillance of autism spectrum disorder. PLoS One 11(12), e0168224 (2016)
Autism Screening. https://www.kaggle.com/fabdelja/autism-screening-for-toddlers
Ramani, G., Selvaraj, S.: A novel approach to analyze a combination of I x J categorical data for estimating road accident risk. Asian J. Inf. Technol. 15(12), 2005–2015 (2016)
Liu, H., Setiono, R.: Chi2: feature selection and discretization of numeric attribute. In: Proceedings of the Seventh IEEE International Conference on Tools with Artificial Intelligence, 5–8 November, p. 388 (1995)
Ramani, G., Selvaraj, S.: A pragmatic approach for refined feature selection for the prediction of road accident severity. Stud. Inf. Control 23(1), 41–52 (2014)
Shanthi, S., Geetha Ramani, R.: Classification of vehicle collision patterns in road accidents using data mining algorithms. Int. J. Comput. Appl. 35(12), 30–37 (2011)
Shanthi, S., Geetha Ramani, R.: Classification of seating position specific patterns in road traffic accident data through data mining techniques. In: Proceedings of Second International Conference on Computer Applications, vol. 5, pp. 98–104 (2012)
Latkowski, T., Stanislaw, O.: Data mining for feature selection in gene expression autism data. Expert Syst. Appl. 42, 864–872 (2015)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Hoeft, F., Walter, E., Lightbody, A.A., Hazlett, H.C., Chang, C., Piven, J., Reiss, A.L.: Neuroanatomical differences in toddler boys with fragile X syndrome and idiopathic autism. Arch. Gen. Psychiatry 68(3), 295–305 (2011)
Price, T., Wee, C.Y., Gao, W., Shen, D.: Multiple-network classification of childhood autism using functional connectivity dynamics. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. LNCS, vol 8675 (2014)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)
Altay, O. Ulas, M.: Prediction of the autism spectrum disorder diagnosis with linear discriminant analysis classifier and K-nearest neighbor in children. In: 6th International Symposium on Digital Forensic and Security (ISDFS), pp. 1–4, Antalya (2018)
Machine Learning in Python. https://scikit-learn.org/stable/
Kotsiantis, S.B.: Supervised machine learning: a review of classification techniques. Informatica 31, 249–268 (2007)
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Selvaraj, S., Palanisamy, P., Parveen, S., Monisha (2020). Autism Spectrum Disorder Prediction Using Machine Learning Algorithms. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_56
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