Machine Learning Classifiers for Autism Spectrum Disorder: A Review

Abstract

Autism Spectrum Disorder (ASD) is a brain development disorder 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 of autism spectrum disorder using the machine learning algorithm is supervised learning. The initial study of the article was collected from website provided articles were in according to this study. The articles were collected from online databases and 16 research articles met the requirements in this study. Based on the results obtained, the most widely used algorithm in the literature study in this study is support vector machine (SVM) of 6S.75%. With the application of machine learning in the case of ASD expected to be able to accelerate and improve accuracy in determining a diagnosis.