2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 2 , Issue 2 , PP: 33-44, 2021 | Cite this article as | XML | Html | PDF | Full Length Article
Qiang Zhang 1 * , Fatemeh Safara 2
Doi: https://doi.org/10.54216/JISIoT.020201
A medical condition that causes disability and early neurological and cognitive condition is autism spectrum disorder (ASD). Gene expression and environment have an impact on this medical condition. Development of diagnostic instruments and skills improved the autism recognition and increased the society awareness about it. To cope with this disorder collaboration between families, service providers, and autistic individuals is a necessity. Early diagnosis of ASD could help in lessening stress, increase adaptation, and support welfare in healthcare systems. Therefore, a large body of research is attempting to provide an intelligent medical diagnostic system to identify and diagnose ASD in early stages using machine learning methods. In this paper, several multilayer perceptron neural network is proposed for ASD detection in healthcare systems. The learning rate is adaptively tuned to achieve the best results. The results show that the approach proposed in this study achieved 99.6% accuracy, which indicates the superiority of the proposed method in identifying and detecting autism disorder in comparison with similar previous methods.
Autism spectrum disorder, Intelligent medical diagnostic system, multilayer perceptron neural network, adaptive learning rate, machine learning
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