Autism unlike other diseases has peculiar symptoms and pre-causes. The symptoms and suspicions are found especially in newly born children, preterm born infants, and children below 12 years. These children have peculiar attributes such as inability to communicate with fellow children, poor speech ability, difficulty in dealing with daily routines and procedures and being oversensitive. This study correlates with the existing work on autism diagnosis techniques by using machine learning methodologies. It further provides the summary of the relevant techniques to validate the existence of autism disorder and strategies used for diagnosis. Various diagnostic methods include behavioural analysis, eye tracking, and neural or brain imaging. The key objective of the chapter is to assess and understand the preliminary causes of the autism spectrum disorder, including analyzing technological support that can be rendered for the early diagnosis of autism.

Autism is a developmental disorder which affects cognition, social and behavioural functionalities of a person. When a person is affected by autism spectrum disorder, he/she will exhibit peculiar behaviours and those symptoms initiate from that patient’s childhood. Early diagnosis of autism is an important and challenging task. Behavioural analysis a well known therapeutic practice can be adopted for earlier diagnosis of autism. Machine learning is a computational methodology, which can be applied to a wide range of applications in-order to obtain efficient outputs. At present machine learning is especially applied in medical applications such as disease prediction. In our study we evaluated various machine learning algorithms [(Naive bayes (NB), Support Vector Machines (SVM) and k-Nearest Neighbours (KNN)] with “k-fold” based cross validation for 3 datasets retrieved from the UCI repository. Additionally we validated the effective accuracy of the estimated results using a clustered cross validation strategy. The process of employing the clustered cross validation scrutinises the parameters which contributes more importance in the dataset. The strategy induces hyper parameter tuning which yields trusted results as it involves double validation. On application of the clustered cross validation for a SVM based model, we obtained an accuracy of 99.6% accuracy for autism child dataset.