Enhancing Diagnosis of Autism With Optimized Machine Learning Models and Personal Characteristic Data.

ABSTRACT: Autism spectrum disorder (ASD) is a developmental disorder, affecting about 1% of the global population. Currently, the only clinical method for diagnosing ASD are standardized ASD tests which require prolonged diagnostic time and increased medical costs. Our objective was to explore the predictive power of personal characteristic data (PCD) from a large well-characterized dataset to improve upon prior diagnostic models of ASD. We extracted six personal characteristics (age, sex, handedness, and three individual measures of IQ) from 851 subjects in the Autism Brain Imaging Data Exchange (ABIDE) database. ABIDE is an international collaborative project that collected data from a large number of ASD patients and typical non-ASD controls from 17 research and clinical institutes. We employed t

SUBMITTER: Parikh MN 

PROVIDER: S-EPMC6384273 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature