Abstract
Autism Spectrum Disorder (ASD) is a state of immature cerebrum progression which resists the normal way of life, including communication, behavior, and sensory ability. Autism can be detected at an early stage with proper advanced methods when it is assumed as a behavioral disease. The screening test is one of the approved processes in detecting Autism Spectrum Disorder (ASD), which is time-consuming as well as extravagant. Using intelligent retrieval and neural-based algorithms, autism can be identified with great efficiency and precision. Different models have been developed consuming this advanced technology in that context, but still, there is a scope for betterment. In this paper, a bunch of methods of machine learning based on Deep Neural Network (DNN), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) has been introduced in the prediction of autism at any age with higher regulation and acceleration. The methods were trained over 10 autism-spectrum quotient (AQ) and several features that can reveal the state of function of mind and behavior. The proposed model shows better accuracy than previous work and also illustrates the comparison between the outcomes of used models.
Keywords
- Autism Spectrum Disorder (ASD)
- Autism Spectrum Quotient (AQ)
- Support Vector Machine (SVM)
- K-Nearest neighbor (KNN)
- Deep Neural Network (DNN)
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Jyoti, O., Islam, N., Faruq, M.O., Siddique, M.A.I., Rahaman, M.H. (2021). Autism Spectrum Disorder Prognosis Using Machine Learning Algorithms: A Comparative Study. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_65
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DOI: https://doi.org/10.1007/978-3-030-68154-8_65
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