Intelligent Frame Work to Predict Autism in Infants using Machine Learning

Abstract

Autistic Spectrum Disorder (ASD) is a behavioral 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 checkup 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 precision, responsiveness, precision and false positive rate (FPR). In the modern day, Autism Spectrum Disorder (ASD) is gaining some momentum quicker than ever. Detecting signs of autism by screening tests is very costly and time consuming. Autism can be identified faster by combining artificial intelligence and Machine Learning (ML).