Asian Journal of Computer Science and Technology (AJCST)
Prediction of Autism Spectrum Disorder Using Supervised Machine Learning AlgorithmsAuthor : T. Lakshmi Praveena and N. V. Muthu Lakshmi
Volume 8 No.3 Special Issue:June 2019 pp 142-145
Autism appears to be a neuro developmental disorder that is visible in the early years. It is a wide-spectrum disorder that indicates that the severity and symptoms can vary from person to person. The Centre for Disease Control found that one in 68 was diagnosed with autism spectrum disorder with increasing numbers in every year. Detection of autism in adults is a cumbersome procedure because in adults, many symptoms can blend with some other mental health, motor impairment disorders so misinterpretation of actual diseases can in turn lead to a terrible life without proper diagnosis and effective treatment mechanisms. Machine learning is a powerful computer tool that supports different application domains Learning complex relationships or patterns from large datasets to draw accurate conclusions. Disease assessment can be done with predictive health data analysis and more appropriate treatment mechanisms that are now a hot area of research. Supervised learning is an important step of Machine learning which uses a rule-based approach by examining empirical data sets to build accurate predictive models. In this paper, decision tree, random forest, SVM, neural networks algorithms are applied on autism spectrum data which have been collected from UCI repository. The results of decision tree, random forest, SVM, neural networks algorithms on autism dataset are presented in this paper in an efficient manner. Analysis performed over these accurate results which will be useful to make right decisions in predicting autism spectrum disorder (ASD) at early stages. Thus, early autism intervention using machine learning techniques opens up a new way for autistic individuals to develop the potential to lead a better life by improving their behavioural and emotional skills.
Autism Spectrum Disorder, Supervised Learning, Decision Trees, Random Forest, SVM, Neural Networks
 A.M. Daniels, R.E. Rosenberg, J.K. Law, C. Lord, W.E. Kaufmann, and P.A. Law, “Stability of initial autism spectrum disorder diagnoses in community settings,” Journal of Autism and Developmental Disorders, Vol. 41, No. 1, pp. 110–121, 2011.
 Tracy A Becerra, Maria L Massillon, Vincent M You, Ashley A Owen-Smith, Frances L Lynch, Phillip M Crawford, MS, Kathryn A Pearson, Magdalena E Pomichowski, Virginia P Quinn, Cathleen K Yoshida, and Lisa A Crone, A Survey of Parents with Children on the Autism Spectrum: Experience with Services and Treatments, [Online] Available at: https://doi.org/10.7812/TPP/16-009
 Caroline P. Whyatt and Elizabeth B. Torres, Autism Research: An Objective Quantitative Review of Progress and Focus Between 1994 and 2015, Doi: 10.3389/fpsyg.2018.01526.
 F. Thabtah, “Machine learning in autistic spectrum disorder behavioral research: A review and ways forward,” Informatics for Health and Social Care, pp. 1–20, 2018.
 Alessandro Crippa ,Christian Salvatore, Paolo Perego, Sara Forti, Maria Nobile, Massimo Molteni, and Isabella Castiglioni, Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities, DOI 10.1007/s10803-015-2379-8.
 B. Auyeung, S. Baron-Cohen, S. Wheelwright, and C. Allison, “The autism spectrum quotient: Childrens version (aq-child),” Journal of autism and developmental disorders, Vol. 38, No. 7, pp. 1230–1240, 2008.
 D. Bone, S.L. Bishop, M.P. Black, M.S. Goodwin, C. Lord, and S.S. Narayanan, “Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion,” Journal of Child Psychology and Psychiatry, Vol. 57, No. 8, pp. 927–937, 2016.
 R. GeethaRamani and K. Sivaselvi, “Autism Spectrum Disorder Identification Using Data Mining Techniques”, International Journal Of Pure And Applied Mathematics, Vol. 117, No. 16, pp. 427-436, 2017.
 Sheena Angra and SachinAhuja, “Machine learning and its, applications: A review”, In proc. International Conference on Big Data Analytics and Computational Intelligence (ICBDAC-2017), DOI: 10.1109/ICBDACI.2017.8070809
 Murat Gök, “A novel machine learning model to predict autism spectrum disorders risk gene”,Neural Computing and Applications,pp.1–7,Springer.
 Matthew J. Maenner, MarshalynYeargin-Allsopp, Kim Van Naarden Braun, Deborah L. Christensen, and Laura A. Schieve, Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder, published: December 21, 2016, [Online] Available at: https://doi.org/10.1371/journal.pone.0168224
 Kiyoharu Takara, and Tsuyoshi Kondo, “Autism spectrum disorder among first-visit depressed adult patients: diagnostic clues from backgrounds and past history”, General Hospital Psychiatry, Elsevier, Vol. 36, pp. 737–742, 2014.
 Hana S. Alarifi and G. S. Young, “Using Multiple Machine Learning Algorithms to Predict Autism in Children”, In proc. Int’l Conf. Artificial Intelligence | ICAI’18, CSREA Press.
 M.S. Mythili, and A.R. Mohamed Shanavas, “A Study on Autism Spectrum Disorders using Classification Techniques”, International Journal of Soft Computing and Engineering (IJSCE), Vol. 4, No.5, Nov.2014.