Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network
Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Baghdad, College of Science for Women
2023-06-01
|
| Series: | مجلة بغداد للعلوم |
| Subjects: | |
| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8564 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839602234705313792 |
|---|---|
| author | Aythem Khairi Kareem Mohammed M. AL-Ani Ahmed Adil Nafea |
| author_facet | Aythem Khairi Kareem Mohammed M. AL-Ani Ahmed Adil Nafea |
| author_sort | Aythem Khairi Kareem |
| collection | DOAJ |
| description |
Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D CNNs have shown improved accuracy in the classification of ASD compared to traditional machine learning algorithms, on all these datasets with higher accuracy of 99.45%, 98.66%, and 90% for Autistic Spectrum Disorder Screening in Data for Adults, Children, and Adolescents respectively as they are better suited for the analysis of time series data commonly used in the diagnosis of this disorder
|
| format | Article |
| id | doaj-art-5b73b921b6cb41efb644d8f91e2aa5a5 |
| institution | Matheson Library |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-5b73b921b6cb41efb644d8f91e2aa5a52025-08-02T08:48:36ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862023-06-01203(Suppl.)10.21123/bsj.2023.8564Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural NetworkAythem Khairi Kareem 0Mohammed M. AL-Ani 1Ahmed Adil Nafea2 Department of Heet Education, General Directorate of Education in Anbar, Ministry of Education, Heet, 31007 Anbar, Iraq Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, MalaysiaCollege of Computer Science and IT, University of Anbar, Ramadi, Iraq Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D CNNs have shown improved accuracy in the classification of ASD compared to traditional machine learning algorithms, on all these datasets with higher accuracy of 99.45%, 98.66%, and 90% for Autistic Spectrum Disorder Screening in Data for Adults, Children, and Adolescents respectively as they are better suited for the analysis of time series data commonly used in the diagnosis of this disorder https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8564Autism Spectrum Disorder, Classification, Deep Learning, Machine Learning, One-Dimensional-Convolutional Neural Network |
| spellingShingle | Aythem Khairi Kareem Mohammed M. AL-Ani Ahmed Adil Nafea Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network مجلة بغداد للعلوم Autism Spectrum Disorder, Classification, Deep Learning, Machine Learning, One-Dimensional-Convolutional Neural Network |
| title | Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network |
| title_full | Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network |
| title_fullStr | Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network |
| title_full_unstemmed | Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network |
| title_short | Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network |
| title_sort | detection of autism spectrum disorder using a 1 dimensional convolutional neural network |
| topic | Autism Spectrum Disorder, Classification, Deep Learning, Machine Learning, One-Dimensional-Convolutional Neural Network |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8564 |
| work_keys_str_mv | AT aythemkhairikareem detectionofautismspectrumdisorderusinga1dimensionalconvolutionalneuralnetwork AT mohammedmalani detectionofautismspectrumdisorderusinga1dimensionalconvolutionalneuralnetwork AT ahmedadilnafea detectionofautismspectrumdisorderusinga1dimensionalconvolutionalneuralnetwork |