Abstract

Background
Machine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have noted pitfalls (Bone et al., 2015). In this work, we fastidiously utilize ML to derive autism spectrum disorder (ASD) instrument algorithms in an attempt to improve upon widely used ASD screening and diagnostic tools.
Methods
The data consisted of Autism Diagnostic Interview‐Revised (ADI‐R) and Social Responsiveness Scale (SRS) scores for 1,264 verbal individuals with ASD and 462 verbal individuals with non‐ASD developmental or psychiatric disorders, split at age 10. Algorithms were created via a robust ML classifier, support vector machine, while targeting best‐estimate clinical diagnosis of ASD versus non‐ASD. Parameter settings were tuned in multiple levels of cross‐validation.
Results
The created algorithms were more effective (higher performing) than the …

Date

January 1, 1970

Authors

Daniel Bone, Somer L Bishop, Matthew P Black, Matthew S Goodwin, Catherine Lord, Shrikanth S Narayanan

Journal

Journal of Child Psychology and Psychiatry

Volume

57

Issue

8

Pages

927-937