Prediction of Autism Spectrum Disorder using Gene Expression

Autism Spectrum Disorder(ASD) is defined by deficits in social communication and repetitive sensory-motor behaviors that are heavily influenced by both genetic factors and environmental triggers. Genetic factors play a vital role in ASD, although environmental conditions also contribute to the disorder. Rare copy-number variations in DNA are a significant risk factor for ASD. The current diagnosis process for ASD involves clinically based standardized tests, resulting in a lengthy and expensive procedure. To improve diagnosis accuracy and speed, machine learning approaches are being implemented in conjunction with traditional methods. We have implemented different machine learning algorithms, such as Support Vector Machine, Linear Regression Analysis, Naive Bayes, and Random Forest, to classify medical data, determine the association between variables, make real-time predictions and signify attribute relationships in computational biology and genetics research