Federated Machine Learning for Multi-Aspect Neuro-developmental Disorders

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dc.contributor.author Shamseddine, Hala
dc.date.accessioned 2022-11-02T11:04:21Z
dc.date.available 2022-11-02T11:04:21Z
dc.date.copyright 2022 en_US
dc.date.issued 2022-08-18
dc.identifier.uri http://hdl.handle.net/10725/14204
dc.description.abstract Autism Spectrum Disorder (ASD) is a neuro-developmental syndrome resulting from alterations in the embryological brain pre-birth. This disorder distinguishes its patients by special socially restricted and repetitive behavior, in addition to specific behavioral traits. Hence, this would possibly deteriorate their social behavior among other individuals, as well as their overall interaction within their community. Moreover, medical research has proved that ASD also affects the facial characteristics of its patients, making the syndrome recognizable from distinctive signs within an individual’s face. Given that as a motivation behind our work, we propose a novel privacy-preserving Federated Learning scheme to predict ASD in a certain individual based on their behavioral and facial features, embedding a merging process of both data features through facial feature extraction, while respecting patient data privacy. After training behavioral and facial image data on Federated Machine Learning models, promising results are achieved, with 70% accuracy for prediction of ASD according to behavioral traits in a federated learning private environment, and a 62% accuracy is reached for prediction of ASD given an image of the patient’s face. Then, we test the behavior of regular as well as federated learning on our merged data, behavioral and facial, where a 65% accuracy is achieved with regular logistic regression model and 63% accuracy with federated learning model. en_US
dc.language.iso en en_US
dc.subject Autism spectrum disorders -- Diagnosis en_US
dc.subject Facial expression -- Computer simulation en_US
dc.subject Machine learning -- Case studies en_US
dc.subject Computer vision in medicine en_US
dc.subject Lebanese American University -- Dissertations en_US
dc.subject Dissertations, Academic en_US
dc.title Federated Machine Learning for Multi-Aspect Neuro-developmental Disorders en_US
dc.type Thesis en_US
dc.title.subtitle Autism Spectrum Disorder (ASD) Detection en_US
dc.term.submitted Summer en_US
dc.author.degree MS in Computer Science en_US
dc.author.school SAS en_US
dc.author.idnumber 201705017 en_US
dc.author.commembers Otoum, Safa
dc.author.commembers Harmanani, Haidar
dc.author.department Computer Science And Mathematics en_US
dc.description.physdesc 1 online resource (xi, 49 leaves): col. ill. en_US
dc.author.advisor Mourad, Azzam
dc.keywords Federated Machine Learning en_US
dc.keywords Autism-Spectrum Disorder en_US
dc.keywords Behavioral and Facial traits en_US
dc.keywords Privacy and Security en_US
dc.description.bibliographiccitations Bibliography: leaves 44-49. en_US
dc.identifier.doi https://doi.org/10.26756/th.2022.508
dc.author.email hala.shamseddine@lau.edu.lb en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php en_US
dc.publisher.institution Lebanese American University en_US
dc.author.affiliation Lebanese American University en_US

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