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J Autism Dev Disord. 2015 Jul;45(7):2146-56. doi: 10.1007/s10803-015-2379-8.

Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities.

Author information

1
Child Psychopathology Unit, Scientific Institute, IRCCS Eugenio Medea, Via Don Luigi Monza 20, 23842, Bosisio Parini, Lecco, Italy, alessandro.crippa@bp.lnf.it.

Abstract

In the present work, we have undertaken a proof-of-concept study to determine whether a simple upper-limb movement could be useful to accurately classify low-functioning children with autism spectrum disorder (ASD) aged 2-4. To answer this question, we developed a supervised machine-learning method to correctly discriminate 15 preschool children with ASD from 15 typically developing children by means of kinematic analysis of a simple reach-to-drop task. Our method reached a maximum classification accuracy of 96.7% with seven features related to the goal-oriented part of the movement. These preliminary findings offer insight into a possible motor signature of ASD that may be potentially useful in identifying a well-defined subset of patients, reducing the clinical heterogeneity within the broad behavioral phenotype.

PMID:
25652603
DOI:
10.1007/s10803-015-2379-8
[Indexed for MEDLINE]

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