  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.
