Description

Identification of autism spectrum disorder (ASD) in children is challenging due to the complexity and heterogeneity of ASD. Spectrum Disorder (ASD) is a behavioural impairment that interferes with the use of auditory, communicative, cognitive, abilities and social skills. ADS was introduce researched using advanced approaches based on machine learning to speed up the diagnostic period or increase the responsiveness, reliability or precision of the diagnosis process. Normal medical check-up data (training data) of the baby, test data is used to classify the child with autism. The assessment results showed that, for both types of datasets, the proposed prediction model produces better results in terms of accuracy, precision, recall, sensitivity, specificity In the modern day Detecting of autism by screening tests is very costly and time consuming. Autism can be identified faster by combining artificial intelligence and Machine Learning (ML).