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Autism spectrum condition (ASC) or autism spectrum disorder (ASD) isprimarily identified with the help of behavioral indications encompassingsocial, sensory and motor characteristics. Although categorized, recurringmotor actions are measured during diagnosis, quantifiable measures thatascertain kinematic physiognomies in the movement configurations of autisticpersons are not adequately studied, hindering the advances in understanding theetiology of motor mutilation. Subject aspects such as behavioral charactersthat influences ASD need further exploration. Presently, limited autismdatasets concomitant with screening ASD are available, and a majority of themare genetic. Hence, in this study, we used a dataset related to autismscreening enveloping ten behavioral and ten personal attributes that have beeneffective in diagnosing ASD cases from controls in behavior science. ASDdiagnosis is time exhaustive and uneconomical. The burgeoning ASD casesworldwide mandate a need for the fast and economical screening tool. Our studyaimed to implement an artificial neural network with the Levenberg-Marquardtalgorithm to detect ASD and examine its predictive accuracy. Consecutively,develop a clinical decision support system for early ASD identification.

Prognosticating Autism Spectrum Disorder Using Artificial Neural Network: Levenberg-Marquardt Algorithm