  Autism spectrum condition (ASC) or autism spectrum disorder (ASD) is
primarily identified with the help of behavioral indications encompassing
social, sensory and motor characteristics. Although categorized, recurring
motor actions are measured during diagnosis, quantifiable measures that
ascertain kinematic physiognomies in the movement configurations of autistic
persons are not adequately studied, hindering the advances in understanding the
etiology of motor mutilation. Subject aspects such as behavioral characters
that influences ASD need further exploration. Presently, limited autism
datasets concomitant with screening ASD are available, and a majority of them
are genetic. Hence, in this study, we used a dataset related to autism
screening enveloping ten behavioral and ten personal attributes that have been
effective in diagnosing ASD cases from controls in behavior science. ASD
diagnosis is time exhaustive and uneconomical. The burgeoning ASD cases
worldwide mandate a need for the fast and economical screening tool. Our study
aimed to implement an artificial neural network with the Levenberg-Marquardt
algorithm to detect ASD and examine its predictive accuracy. Consecutively,
develop a clinical decision support system for early ASD identification.
