Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm.

ABSTRACT: DSM-5 Autism Spectrum Disorder (ASD) comprises a set of neurodevelopmental disorders characterized by deficits in social communication and interaction and repetitive behaviors or restricted interests, and may both affect and be affected by multiple cognitive mechanisms. This study attempts to identify and characterize cognitive subtypes within the ASD population using our Functional Random Forest (FRF) machine learning classification model. This model trained a traditional random forest model on measures from seven tasks that reflect multiple levels of information processing. 47 ASD diagnosed and 58 typically developing (TD) children between the ages of 9 and 13 participated in this study. Our RF model was 72.7% accurate, with 80.7% specificity and 63.1% sensitivity. Using the random fores

SUBMITTER: Feczko E 

PROVIDER: S-EPMC5969914 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature