Exploratory Data Analysis and Decision Tree Modeling for Autism Spectrum Disorder: Machine Learning Approach

As the early diagnosis of autism spectrum disorder (ASD) is critical high accuracy machine learning can be applied to achieve technology based diagnosis. In this context, present study demonstrates approach for ASD using decision tree (DT) modeling. The dataset employed in comprises two classes adults with a sample size 704 instances. DT model entails recursive partitioning implemented “rpart” package R. optimum derived by tuning parameters such as Min split, bucket, Max depth, and complexity. performance evaluated terms mean square error estimate rate.