Diagnosing Autism Spectrum Disorder in Children

Abstract

In recent years, classifiers have been shown to provide highly accurate results in predicting problems; however, the success relies on the classifier as well as the employed dataset. In this study, the success of various classifiers for predicting autism spectrum disorder cases is analysed in terms of different metrics obtained both after testing on the same set and after applying 10-fold cross validation. When the same dataset has been used for the training and testing steps, all the algorithms have shown excellent results. Nevertheless, when 10-fold cross-validation has been applied, sequential minimal optimization has become the most successful classifier in terms of all the performance metrics. Although the results have proven the success of classifiers in predicting problems such as the one addressed in this study, autism spectrum disorder is a complex disorder, and the tools employing classifiers should be used under the supervision of certified professionals or clinicians.