Classification of Autism Spectrum Disorder Data using Machine Learning Techniques
V.Jalaja Jayalakshmi1, V.Geetha2, R.Vivek3
1V.Jalaja Jayalakshmi, Assistant Professor, Department of Computer Applications, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
2V.Geetha, Professor, Department of Computer Applications, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
3R.Vivek, Student, Department of Computer Applications, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
Manuscript received on 16 August 2019 | Revised Manuscript received on 28 August 2019 | Manuscript Published on 06 September 2019 | PP: 565-569 | Volume-8 Issue- 6S, August 2019 | Retrieval Number: F11140886S19/19©BEIESP | DOI: 10.35940/ijeat.F1114.0886S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Autism is a neuro-developmental disability that affects human communication and behaviour. It is a condition that is associated with the complex disorder of the brain which can lead to significant changes in social interaction and behaviour of a human being. Machine learning techniques are being applied to autism data sets to discover useful hidden patterns and to construct predictive models for detecting its risk. This paper focuses on finding the best machine learning classifier on the UCI autism disorder data set for identifying the main factors associated with autism. The results obtained using Multilayer Perceptron, Naive Bayes Classifier and Bayesian Network were compared with J48 Decision tree algorithm. The superiority of Multilayer Perceptron over the well known classification algorithms in predicting the autism risk is established in this paper.
Keywords: Machine Learning, Classification, Accuracy, Autism.
Scope of the Article: Classification