  Autism Spectrum Disorder (ASD) is one neuro developmental disorder that is
now widespread in the world. ASD persists throughout the life of an individual,
impacting the way they behave and communicate, resulting to notable deficits
consisting of social life retardation, repeated behavioural traits and a
restriction in their interests. Early detection of the disorder helps in the
onset treatment and helps one to lead a normal life. There are clinical
approaches used in detection of autism, relying on behavioural data and in
worst cases, neuroimaging. Quantitative methods involving machine learning have
been studied and developed to overcome issues with clinical approaches. These
quantitative methods rely on machine learning, with some complex methods based
on deep learning developed to accelerate detection and diagnosis of ASD. These
literature is aimed at exploring most state-of-the-art probabilistic methods in
use today, characterizing them with the type of dataset they're most applied
on, their accuracy according to their novel research and how well they are
suited in ASD classification. The findings will purposely serve as a benchmark
in selection of the model to use when performing ASD detection.
