A Review of Machine Learning Models for Predicting Autism Spectrum Disorder

Kanchanamala P, G.Leela Sagar
<span title="2019-02-28">2019</span> <i title="BioAxis DNA Research Centre"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/grcyvowbjbbnfdmv2azpa6ltf4" style="color: black;">Helix</a> </i> &nbsp;
Autism spectrum disorder (ASD) is a neurological and developmental disorder that impacts the behavior of the person throughout a life. Every individual with ASD exhibits the difficulty in communication and social interaction with restricted interests and repetitive behaviors. There is no standard diagnosis and treatment for ASD. The social behavior of the children can be improved by early identification of autism spectrum disorder (ASD). A vigorous predictive mechanism desires features such as
more &raquo; ... acial expressions, eye movement, and brain activity images. The research embody diverse approaches to classify and categorize cognitive disorders affected children based on fMRI scan images, facial expressions, gage contingent eye tracking and stereotypical motor movement. Predictive models can be developed to determine if the new patients are developing with ASD using historical patient data. This paper explores the machine learning models built using structured clinical patient data for predicting the ASD subjects.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.29042/2019-4797-4801">doi:10.29042/2019-4797-4801</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3qyltbuuqnamjmbbx5ywtbozpi">fatcat:3qyltbuuqnamjmbbx5ywtbozpi</a> </span>
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