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.
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References
Autism spectrum disorders (2019). https://www.who.int/news-room/fact-sheets/detail/autism-spectrum-disorders. Accessed 21 July 2020
Prevalence of autism spectrum disorder among children in select countries worldwide as of 2020 (2020). https://www.statista.com/statistics/676354/autism-rate-among-children-select-countries-worldwide/. Accessed 15 Aug 2020
Raj, S., Masood, S.: Analysis and detection of autism spectrum disorder using machine learning techniques. Procedia Comput. Sci. 167, 994–1004 (2020)
Erkan, U., Thanh, D.N.: Autism spectrum disorder detection with machine learning methods. Curr. Psychiatry Res. Rev. Formerly Curr. Psychiatry Rev. 15(4), 297–308 (2019)
Wang, H., Li, L., Chi, L., Zhao, Z.: Autism screening using deep embedding representation. In: International Conference on Computational Science, pp. 160–173. Springer (2019)
Lerthattasilp, T., Tanprasertkul, C.,  Chunsuwan, I.: Development of clinical prediction rule for diagnosis of autistic spectrum disorder in children. Mental Illness (2020)
Hyde, K.K., Novack, M.N., LaHaye, N., Parlett-Pelleriti, C., Anden, R., Dixon, D.R., Linstead, E.: Applications of supervised machine learning in autism spectrum disorder research: a review. Rev. J. Autism Dev. Disord. 6(2), 128–146 (2019)
Li, B., Sharma, A., Meng, J., Purushwalkam, S., Gowen, E.: Applying machine learning to identify autistic adults using imitation: an exploratory study. PloS one 12(8), e0182652 (2017)
Baranwal, A., Vanitha, M.: Autistic spectrum disorder screening: prediction with machine learning models. In: 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), pp. 1–7. IEEE (2020)
Shuvo, S.B., Ghosh, J., Oyshi, A.: A data mining based approach to predict autism spectrum disorder considering behavioral attributes. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–5. IEEE (2019)
Geng, X. Kang, X., Wong, P.C.: Autism spectrum disorder risk prediction: a systematic review of behavioral and neural investigations (2020)
Omar, K.S., Mondal, P., Khan, N.S., Rizvi, M.R.K., Islam, M.N.: A machine learning approach to predict autism spectrum disorder. In: 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1–6. IEEE (2019)
Yang, X., Li, F., Liu, H.: A comparative study of dnn-based models for blind image quality prediction. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 1019–1023. IEEE (2019)
Vasant, P., Zelinka, I., Weber, G.-W.: Intelligent Computing & Optimization, vol. 866. Springer, Cham (2018)
Zhang, Y.: Support vector machine classification algorithm and its application. In: International Conference on Information Computing and Applications, pp. 179–186. Springer (2012)
Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K.: Knn model-based approach in classification. In: OTM Confederated International Conferences" On the Move to Meaningful Internet Systems", pp. 986–996. Springer (2003)
Altay, O., Ulas, M.: Prediction of the autism spectrum disorder diagnosis with linear discriminant analysis classifier and k-nearest neighbor in children. In:2018 6th International Symposium on Digital Forensic and Security (ISDFS), pp. 1–4. IEEE (2018)
Niu, K., Guo, J., Pan, Y., Gao, X., Peng, X., Li, N., Li, H.: Multichannel deep attention neural networks for the classification of autism spectrum disorder using neuroimaging and personal characteristic data. Complexity 2020 (2020)
Al Diabat, M.,  Al-Shanableh, N.: Ensemble learning model for screening autism in children (2019)
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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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