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

Autor/inn/en	Crippa, Alessandro; Salvatore, Christian; Perego, Paolo; Forti, Sara; Nobile, Maria; Molteni, Massimo; Castiglioni, Isabella
Titel	Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities
Quelle	In: Journal of Autism and Developmental Disorders, 45 (2015) 7, S.2146-2156 (11 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0162-3257
DOI	10.1007/s10803-015-2379-8
Schlagwörter	Children; Autism; Psychomotor Skills; Task Analysis; Identification; Classification; Preschool Children; Patients; Genetics; Motor Reactions; Accuracy; Equipment; Evaluation Methods; Goal Orientation + Suchen Sie Ihr Suchwort? Child; Kind; Kinder; Autismus; Psychomotorische Aktivität; Aufgabenanalyse; Identifikation; Identifizierung; Classification system; Klassifikation; Klassifikationssystem; Pre-school age; Preschool age; Children; Pre-school education; Preschool education; Vorschulalter; Vorschulkind; Vorschulkinder; Vorschulerziehung; Vorschule; Patient; Humangenetik; Zielorientierung; Zielvorstellung
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01