A Comparative Study of Machine Learning Techniques for Autism Spectrum Disorder (ASD) Detection

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dc.contributor.advisorDr. Atiqur Rahman
dc.contributor.authorZahid Hasan
dc.contributor.authorTowsif Muhtadi Khan
dc.contributor.id1911509042
dc.contributor.id1911576042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2024-05-07
dc.date.accessioned2024-05-07T05:19:45Z
dc.date.available2024-05-07T05:19:45Z
dc.date.issued2022
dc.description.abstractAutism Spectrum Disorder (ASD) is a developmental impairment caused by brain differences. ASD patients may have a recognized difference, such as a genetic disease [1]. Autism can be diagnosed at any age and is referred to as a "behavioral disorder" since symptoms often develop within the first two years of life [1]. In this study, we have implemented different classifier algorithms such as Decision Tree Classifier, Logistic Regression, and Multi-layer Perceptron classifier (MLP Classifier) for detecting ASD in three types of people - adults, adolescents, and children. The proposed techniques are evaluated on publicly available three different non-clinically ASD datasets [2]. After applying various machine learning techniques and handling missing values, results strongly suggest that ANN-based prediction models work better on all these datasets with higher accuracy of 98.58%, 98.30%, and 95.24% for Autistic Spectrum Disorder Screening Data for Adults, Children, and Adolescents, respectively.
dc.description.degreeUndergraduate
dc.identifier.cd600000041
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/603
dc.language.isoen_US
dc.publisherNorth South University
dc.rights© NSU Library
dc.subjectTECHNOLOGY::Electrical engineering, electronics and photonics::Electrical engineering
dc.titleA Comparative Study of Machine Learning Techniques for Autism Spectrum Disorder (ASD) Detection
dc.typeProject
oaire.citation.endPage63
oaire.citation.startPage1
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