Machine Learning Classifiers for Autism Spectrum Disorder: Review

Eman, Dadang and Emanuel, Andi Wahju Rahardjo (2019) Machine Learning Classifiers for Autism Spectrum Disorder: Review. In: Proceedings 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). UNIVERSITAS AMIKOM YOGYAKARTA, Yogyakarta, Indonesia, pp. 1-6.

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Abstract

Autism Spectrum Disorder (ASD) is abrain evelopment disorder that affects the ability to communicate and interact socially. There have been many studies using machine learning methods to classify autism including support vector machines, decision trees, naïve Bayes, random forests, logistic regression, K-nearest Neighbors and others. In this study provides a review on autism spectrum disorder by using a machine learning algorithm that is supervised learning. The initial study of the article was collected from a website provided articles were in according with this study, after going through the process of selecting articles 11 articles were eligible in this study. Based on the results obtained, that the most widely used algorithm in the literature study in this study is support vector machine (SVM) of 63.63%, with the application of machine learning in the case of ASD expected to be able to accelerate and improve accuracy in determining a diagnosis

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