2019 |
Misman, M F; Samah, A A; Ezudin, F A; Majid, H A; Shah, Z A; Hashim, H; Harun, M F Classification of adults with autism spectrum disorder using deep neural network Conference Institute of Electrical and Electronics Engineers Inc., 2019, ISBN: 9781728130415, (cited By 0). Abstract | Links | BibTeX | Tags: Autism Spectrum Disorders, Brain Disorders, Classification (of information), Classification Accuracy, Classification Methods, Clinical Tests, Cognitive Skill, Computer Aided Diagnosis, Deep Learning, Deep Neural Networks, Diseases, Learning, Machine Learning Methods, Screening Data, Support Vector Machines @conference{Misman201929, title = {Classification of adults with autism spectrum disorder using deep neural network}, author = {M F Misman and A A Samah and F A Ezudin and H A Majid and Z A Shah and H Hashim and M F Harun}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079349811&doi=10.1109%2fAiDAS47888.2019.8970823&partnerID=40&md5=dd727e950667359680a6dbcc4855422f}, doi = {10.1109/AiDAS47888.2019.8970823}, isbn = {9781728130415}, year = {2019}, date = {2019-01-01}, journal = {Proceedings - 2019 1st International Conference on Artificial Intelligence and Data Sciences, AiDAS 2019}, pages = {29-34}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {Autism Spectrum Disorder (ASD) is a developmental brain disorder that causes deficits in linguistic, communicative, and cognitive skills as well as social skills. Various application of Machine Learning has been applied apart from the clinical tests available, which has increased the performance in the diagnosis of this disorder. In this study, we applied the Deep Neural Network (DNN) architecture, which has been a popular method in recent years and proved to improve classification accuracy. This study aims to analyse the performance of DNN model in the diagnosis of ASD in terms of classification accuracy by using two datasets of adult ASD screening data. The results are then compared with the previous Machine Learning method by another researcher, which is Support Vector Machine (SVM). The accuracy achieved by the DNN model in the classification of ASD diagnosis is 99.40% on the first dataset and achieved 96.08% on the second dataset. Meanwhile, the SVM model achieved an accuracy of 95.24% and 95.08% using the first and second data, respectively. The results show that ASD cases can be accurately identified by implementing the DNN classification method using ASD adult screening data. © 2019 IEEE.}, note = {cited By 0}, keywords = {Autism Spectrum Disorders, Brain Disorders, Classification (of information), Classification Accuracy, Classification Methods, Clinical Tests, Cognitive Skill, Computer Aided Diagnosis, Deep Learning, Deep Neural Networks, Diseases, Learning, Machine Learning Methods, Screening Data, Support Vector Machines}, pubstate = {published}, tppubtype = {conference} } Autism Spectrum Disorder (ASD) is a developmental brain disorder that causes deficits in linguistic, communicative, and cognitive skills as well as social skills. Various application of Machine Learning has been applied apart from the clinical tests available, which has increased the performance in the diagnosis of this disorder. In this study, we applied the Deep Neural Network (DNN) architecture, which has been a popular method in recent years and proved to improve classification accuracy. This study aims to analyse the performance of DNN model in the diagnosis of ASD in terms of classification accuracy by using two datasets of adult ASD screening data. The results are then compared with the previous Machine Learning method by another researcher, which is Support Vector Machine (SVM). The accuracy achieved by the DNN model in the classification of ASD diagnosis is 99.40% on the first dataset and achieved 96.08% on the second dataset. Meanwhile, the SVM model achieved an accuracy of 95.24% and 95.08% using the first and second data, respectively. The results show that ASD cases can be accurately identified by implementing the DNN classification method using ASD adult screening data. © 2019 IEEE. |
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2019 |
Classification of adults with autism spectrum disorder using deep neural network Conference Institute of Electrical and Electronics Engineers Inc., 2019, ISBN: 9781728130415, (cited By 0). |