Detection of autism spectrum disorder (ASD) in children and adults using machine learning.
Sci Rep
; 13(1): 9605, 2023 06 13.
Article
em En
| MEDLINE
| ID: mdl-37311766
ABSTRACT
Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can prevent from its severity and prolonged effects. Federated learning (FL) is one of the most recent techniques that can be applied for accurate ASD diagnoses in early stages or prevention of its long-term effects. In this article, FL technique has been uniquely applied for autism detection by training two different ML classifiers including logistic regression and support vector machine locally for classification of ASD factors and detection of ASD in children and adults. Due to FL, results obtained from these classifiers have been transmitted to central server where meta classifier is trained to determine which approach is most accurate in the detection of ASD in children and adults. Four different ASD patient datasets, each containing more than 600 records of effected children and adults have been obtained from different repository for features extraction. The proposed model predicted ASD with 98% accuracy (in children) and 81% accuracy (in adults).
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Transtorno Autístico
/
Transtorno do Espectro Autista
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Screening_studies
Limite:
Adult
/
Child
/
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article