Detecting Autism Spectrum Disorder using Machine Learning

TL;DR

This paper evaluates various machine learning classifiers to improve early detection of Autism Spectrum Disorder by analyzing datasets across different age groups, identifying the most effective algorithms and key attributes.

Contribution

It identifies the best classifier (SMO-based SVM) and attribute selection method (Relief Attributes) for ASD diagnosis using machine learning.

Findings

SMO-based SVM outperforms other classifiers in accuracy

Relief Attributes effectively identifies significant ASD traits

Machine learning can enhance early ASD detection

Abstract

Autism Spectrum Disorder (ASD), which is a neuro development disorder, is often accompanied by sensory issues such an over sensitivity or under sensitivity to sounds and smells or touch. Although its main cause is genetics in nature, early detection and treatment can help to improve the conditions. In recent years, machine learning based intelligent diagnosis has been evolved to complement the traditional clinical methods which can be time consuming and expensive. The focus of this paper is to find out the most significant traits and automate the diagnosis process using available classification techniques for improved diagnosis purpose. We have analyzed ASD datasets of Toddler, Child, Adolescent and Adult. We determine the best performing classifier for these binary datasets using the evaluation metrics recall, precision, F-measures and classification errors. Our finding shows that Sequential minimal optimization (SMO) based Support Vector Machines (SVM) classifier outperforms all other benchmark machine learning algorithms in terms of accuracy during the detection of ASD cases and produces less classification errors compared to other algorithms. Also, we find that Relief Attributes algorithm is the best to identify the most significant attributes in ASD datasets.