Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities
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| Title: | Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities |
|---|---|
| Language: | English |
| Authors: | Crippa, Alessandro, Salvatore, Christian, Perego, Paolo |
| Source: | Journal of Autism and Developmental Disorders. Jul 2015 45(7):2146-2156. |
| Availability: | Springer. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: service-ny@springer.com; Web site: http://www.springerlink.com |
| Peer Reviewed: | Y |
| Page Count: | 11 |
| Publication Date: | 2015 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Children, Autism, Psychomotor Skills, Task Analysis, Identification, Classification, Preschool Children, Patients, Genetics, Motor Reactions, Accuracy, Equipment, Evaluation Methods, Goal Orientation |
| DOI: | 10.1007/s10803-015-2379-8 |
| ISSN: | 0162-3257 |
| 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. |
| Abstractor: | As Provided |
| Number of References: | 48 |
| Entry Date: | 2015 |
| Accession Number: | EJ1065200 |
| Database: | ERIC |
| 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. |
|---|---|
| ISSN: | 0162-3257 |
| DOI: | 10.1007/s10803-015-2379-8 |
