Research Article | Open Access
Child Behavioral Analysis: Machine Learning based Investigation for Autism Screening and Early Diagnosis
N. Ajaypradeep Dr.R. Sasikala
Pages: 1199-1209
Abstract
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
Keywords
Autism, Behavioural Analysis Machine Learning, Early Diagnosis, Children.