Thesis (M.Sc.) - Cairo University - Faculty of Computers and Artificial Intelligence - Department of Information Systems

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that causes deficits incognition,communication and socialskills.asd,however,is a highly heterogeneous disorder.this heterogeneity has made identifyingt heetiology of asd a particularly difficult challenge,as patients exhibit a wide spectrum of symptoms with out anyunifying genetic or environmental factors to account for the disorder. for better understanding of asd, it is paramount to identify potential genetic and environmental risk factors that are comorbid with it. Identifying such factors is of great importance to determine potential causes for the disorder,and underst and its heterogeneity .existing large{u2013}scale datasets offer an opportunity for computer scientists to under take this task by utilizing mach in elearning to reliably and efficiently obtain insights about potential ASD risk factors,which would in turn assist inguiding research in the field. moreover,the large sample size of these datasets helps to avoid the pitfalls and biased results associated with studying a heterogeneous disorder througha small sample size

Issued also as CD