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Classifying autism spectrum disorder using machine learning through ABIDE dataset
- 1 College of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an, Shaanxi, 710054, China
- 2 College of Artificial Intelligence, Tianjin University of Science & Technology, Tian-jin, 300457, China
- 3 Design and Technology, Parsons School of Design, The New School, New York, 10003, USA
* Author to whom correspondence should be addressed.
Abstract
A neurodevelopmental disorder named autism spectrum disorder (ASD) is challenging to diagnose. The prevailing diagnostic manner is based merely on the behavioral measure-ment with a high tendency of misdiagnosis. People require an advanced method to make more quantitative diagnosis. In this paper, two deep learning architectures were explored with the machine learning methods. The Mixup method was used to augment the original functional Magnetic Resonance Imaging data. Features of the data extracted by two dif-ferent kinds of autoencoders which are Sparse Autoencoder and Variational Autoencoder were used as inputs of two deep neural networks functioning as classifiers respectively. The models can classify patients with ASD from typical control subjects with the accura-cy of 75.5% and 75.2% respectively, which outperformed the other state-of-the-art meth-od by 4.7% and 4.4%. The further significance of this project is to help develop our per-ception of the neurobiological foundation of the ASD.
Keywords
brain disorder, autism spectrum disorder, machine learning, deep neural network, autoen-coder
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Cite this article
Han,W.;Shao,W.;Wang,Y. (2023). Classifying autism spectrum disorder using machine learning through ABIDE dataset. Applied and Computational Engineering,2,31-44.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)
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