Digital Platform for Monitoring and Comprehensive Support of Children with Autism Spectrum Disorders

Nigmatullina, Irina A.; Sheymardanov, Shamil; Abramskiy, M.

doi:10.1007/978-981-19-7660-5_50

Intelligent Sustainable Systems

2023

DOI: 10.1007/978-981-19-7660-5_50

|View full text |Cite

Digital Platform for Monitoring and Comprehensive Support of Children with Autism Spectrum Disorders

Irina A. Nigmatullina

Shamil Sheymardanov

M. Abramskiy

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, network issues will not be raised as only small sized local data model is travelling over the network towards central server instead of huge data 16 . Many researchers have applied FL for detection of multiple neurological disorders 17 . Ali et al 18 have applied FL for the detection of colon cancer using pixel level segmentation dataset.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Detection of autism spectrum disorder (ASD) in children and adults using machine learning

Farooq

Tehseen

Sabir

et al. 2023

Sci Rep

View full text Add to dashboard Cite

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).

show abstract

mentioning

confidence: 99%

“…Ghosh et al 19 have applied FL for medical image segmentation. Nigmatullina et al 17 proposed a digital platform to monitor and support children with ASD using FL. Novelty of our work is the application of FL technique for detection of ASD in both children and adults.…”

mentioning

confidence: 99%

Detection of autism spectrum disorder (ASD) in children and adults using machine learning

Farooq

Tehseen

Sabir

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Digital Platform for Monitoring and Comprehensive Support of Children with Autism Spectrum Disorders

Cited by 1 publication

References 16 publications

Detection of autism spectrum disorder (ASD) in children and adults using machine learning

Detection of autism spectrum disorder (ASD) in children and adults using machine learning

Contact Info

Product

Resources

About