Novel Framework for Autism Spectrum Disorder Identification and Tailored Education With Effective Data Mining and Ensemble Learning Techniques Article Swipe

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Fahima Hajjej , Sarra Ayouni , Manal Abdullah Alohali , Mohamed Maddeh ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.1109/access.2024.3349988

Autism Spectrum Disorder (ASD) is a neurological condition that significantly affects cognitive abilities, language comprehension, object recognition, interpersonal skills, and communication capabilities. Its primary origin is genetic, and early detection and intervention can mitigate the need for costly medical procedures and lengthy examinations for individuals affected by ASD. Autism Spectrum Disorder is highly diverse, with each affected child being unique. It is often stated that no two autistic children are alike, meaning that what benefits one child may not be suitable for another. An effective teaching approach may be challenging to determine for a child with autism. Two ASD screening datasets of toddlers are merged in this study. The Synthetic Minority Oversampling Technique (SMOTE) method to balance the dataset, followed by feature selection methods. The research introduces a two-phase system: the first phase employs various machine learning models, including an ensemble of random forest and XGBoost classifiers that 94% accurate in ASD identification. In the second phase, the study focuses on identifying appropriate teaching methods for children with ASD by evaluating their physical, verbal, and behavioural performance. This research aims to provide personalized educational approaches for individuals with ASD, harnessing machine learning to enhance precision in addressing their unique needs.

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Artificial Intelligence

Developmental Psychology

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Autism spectrum disorder Autism Computer science Identification (biology) Machine learning Artificial intelligence Random forest Ensemble learning Feature (linguistics) Comprehension Social skills Cognition Intervention (counseling) Interpersonal communication Psychology Developmental psychology Psychiatry Linguistics Social psychology Philosophy Biology Programming language Botany

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Type: article
Language: en
Landing Page: https://doi.org/10.1109/access.2024.3349988
PDF: https://ieeexplore.ieee.org/ielx7/6287639/6514899/10380587.pdf
OA Status: gold
Cited By: 34
References: 51
Related Works: 10
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DOI: https://doi.org/10.1109/access.2024.3349988

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Title: Novel Framework for Autism Spectrum Disorder Identification and Tailored Education With Effective Data Mining and Ensemble Learning Techniques

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Language: en

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Publication year: 2024

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Publication date: 2024-01-01

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Authors: Fahima Hajjej, Sarra Ayouni, Manal Abdullah Alohali, Mohamed Maddeh

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Landing page: https://doi.org/10.1109/access.2024.3349988

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Concepts: Autism spectrum disorder, Autism, Computer science, Identification (biology), Machine learning, Artificial intelligence, Random forest, Ensemble learning, Feature (linguistics), Comprehension, Social skills, Cognition, Intervention (counseling), Interpersonal communication, Psychology, Developmental psychology, Psychiatry, Linguistics, Social psychology, Philosophy, Biology, Programming language, Botany

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Cited by: 34

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Citations by year (recent): 2025: 20, 2024: 14

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