Predictive Model for Autistic Spectrum Disorder with Neural Network Application

Jannie Fleur V. Oraño, Geraldine B. Mangmang, James Arnold E. Nogra

Abstract


The rise in the number of Autistic Spectrum Disorder (ASD) cases across the world reveals that there is a pressing need to develop and implement effective screening methods. The main concern of this study is to formulate a predictive model to detect ASD using data mining technique and neural network. Data mining technique was used to analyze the identified instances using WEKA. A total of 10261 sample data were utilized in this study. Eighty percent of the data was used for training the neural network and twenty percent of the data was used for testing. The classification whether the child has autism or not based on the given patterns was carried out using back propagation neural network. The calculation of the system resulted in up to 98.37% accuracy. It can be concluded that the back propagation neural network was able to effectively detect ASD.

Keywords


ASD; Backpropagation; Artificial Neural Network; Data mining; WEKA; Cross-validation

References


Ahuja, R., & Kaur, D.(2014). Autism and its classification techniques-a review. International Journal of Computer Science and Information Technologies, 5 (2),2166-2170

Autism and developmental disabilities monitoring network. Retrieved

December 2018 from https://www.cdc.gov/ncbddd/autism/addm-communityreport/ documents/ addm-community-report-2018-h.pdf

Bi, X. A., Liu, Y., Jiang, Q., Shu, Q., Sun, Q., & Dai, J. (2018). The diagnosis of autism spectrum disorder based on the random neural network cluster. Frontiers in Human Neuroscience, 12(257), 1-10.

Bolte, S. (2014). Is autism curable?. ¨Developmental Medicine & Child Neurology, 56(10), 927-931.

Centers for Disease Control and Prevention. Attention-Deficit Hyperactivity Disorder (ADHD) Retrieved December 2018 from

https://www.cdc.gov/ncbddd/autism.

Crippa, A., Salvatore, C., Perego, P., Forti, S., Nobile, M., Molteni, M., & Castiglioni, I. (2015). Use of machine learning to identify children with autism and their motor abnormalities. Journal of Autism and Developmental Disorders, 45(7), 2146-2156.

Detwiler, L. (2014). Using backpropagation neural networks for the prediction of residual shear strength of cohesive soils. University of Vermont [undergraduate thesis]. Retrieved from https://scholarworks.uvm.edu/cgi/viewcontent.cgi?referer=https://www.google.com/&httpsredir=1&article=1043&context=hcoltheses

Grossi, E., Veggo, F., Narzisi, A., Compare, A., & Muratori, F. (2016). Pregnancy risk factors in autism: a pilot study with artificial neural networks. Pediatric Research, 79(2), 339-347.

Hazlett, H. C., Gu, H., Munsell, B. C., Kim, S. H., Styner, M., Wolff, J. J., ... & Collins, D. L. (2017). Early brain development in infants at high risk for autism spectrum disorder. Nature, 542(7641), 348-351.

Kalmegh, S. (2015). Analysis of WEKA data mining algorithm REPTree, Simple CART and RandomTree for classification of Indian news. International Journal of Innovative Science Engineering and

Technology , 2(2), 438-446.

Liu, Y., Jing, W., & Xu, L. (2016). Parallelizing backpropagation neural

network using MapReduce and cascading model. Computational Intelligence and Neuroscience, 2, 1-11

Mishra, R., & Bhatnagar, D. (2017). Analyzing the social awareness in autistic children trained through multimedia intervention tool using data mining. International Journal of Advanced Computer Science

and Applications, 8(8), 276-280.

My child without limits. Retrieved December 2018 from http://www.mychildwithoutlimits.org/understand/autism/comm

on-health-problems-in- children/.

Mythili, M. S., & Shanavas, A. M. (2014). A study on autism spectrum

disorders using classification techniques. International Journal of Soft Computing and Engineering, 4(5), 88-91.

National Institute of Metal Health. Autism spectrum disorder. Retrieved from https://www.nimh.nih.gov/health/topics/autism-spectrum-disorders-asd/index.shtml].

Rad, N. M., Bizzego, A., Kia, S. M., Jurman, G., Venuti, P., & Furlanello, C. (2015). Convolutional neural network for stereotypical motor movement detection in autism. Conference Paper presented at

th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging. Montreal:Canada

Saduf, M. A. W. (2013). Comparative study of back propagation learning algorithms for neural networks. International Journal of

Advanced Research in Computer Science and Software Engineering, 3(12), 1151-1156.

Stergiou, C., Siganos, D., Neural networks. Retrieved December 2018 from https://www.doc.ic.ac.uk/∼nd/surprise96/journal/vol4/cs11/report.html#What%20is%20a%20Neural%20Network

Sunsirikul, S., Achalakul, T. (2010). Associative classification mining in the behavior study of autism spectrum disorder. Paper presented at Computer and Automation Engineering, 2nd International Conference. Singapore.

UCI machine learning reposity: Autistic spectrum disorder screening

data for children [Data set]. Retrieved March 2017 from https://archive.ics.uci.edu/ml/datasets/Autistic+Spectrum+Disorder+Screening+Data

Van Hieu, N., & Hien, N. L. H. (2018). Artificial neural network and fuzzy logic approach to diagnose autism spectrum disorder. International Research Journal of Engineering and Technology, 5(6), 1-7.

Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data mining: Practical machine learning tools and techniques. Cambridge, MA: Todd Green


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