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

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...

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Detalles Bibliográficos
Publicado en:	Journal of Autism & Developmental Disorders Vol. 45; no. 7; pp. 2146 - 2157
Autores principales:	Crippa, Alessandro, Salvatore, Christian, Perego, Paolo, Forti, Sara, Nobile, Maria, Molteni, Massimo, Castiglioni, Isabella
Formato:	equations & formulas pictorial research tables/charts Journal Article
Publicado:	Springer Nature Jul2015
Acceso en línea:	Ver este registro en EBSCOhost

Descripción
Sumario:	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.