2399 953
Full Length Article
Journal of Intelligent Systems and Internet of Things
Volume 2 , Issue 2, PP: 33-44 , 2021 | Cite this article as | XML | Html |PDF

Title

Autism Spectrum Diagnosis using Adaptive Learning Algorithm for Multiple MLP Classifier

  Fatemeh Safara 1 *

1  Department of Computer Engineering, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran
    (fsafara@yahoo.com)


Doi   :   https://doi.org/10.54216/JISIoT.020201

Received: January 12, 2021 Accepted: July 10, 2021

Abstract :

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.

Keywords :

Autism spectrum disorder , Intelligent medical diagnostic system , multilayer perceptron neural network , adaptive learning rate , machine learning

References :

[1]       S. Oueida, Y. Kotb, M. Aloqaily, Y. Jararweh, and T. Baker, "An edge computing based smart healthcare framework for resource management," Sensors, vol. 18, no. 12, p. 4307, 2018.

[2]       M.-C. Lai, E. Anagnostou, M. Wiznitzer, C. Allison, and S. Baron-Cohen, "Evidence-based support for autistic people across the lifespan: maximising potential, minimising barriers, and optimising the person–environment fit," The Lancet Neurology, 2020.

[3]       B. Deebak, F. Al-Turjman, M. Aloqaily, and O. Alfandi, "An Authentic-Based Privacy Preservation Protocol for Smart e-Healthcare Systems in IoT," IEEE Access, vol. 7, pp. 135632-135649, 2019.

[4]       S. Oueida, M. Aloqaily, and S. Ionescu, "A smart healthcare reward model for resource allocation in smart city," Multimedia Tools and Applications, vol. 78, no. 17, pp. 24573-24594, 2019.

[5]       G. Todorov, N. Nikolov, Y. Sofronov, N. Gabrovski, M. Laleva, and T. Gavrilov, "Computer Aided Design of Customized Implants Based on CT-Scan Data and Virtual Prototypes," in International Conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures, 2019: Springer, pp. 339-346. 

[6]       M. Al-Khafajiy et al., "Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model," ACM Transactions on Internet Technology.

[7]       K. Sarmukadam, V. Bitsika, C. F. Sharpley, M. M. McMillan, and L. L. Agnew, "Comparing different EEG connectivity methods in young males with ASD," Behavioural Brain Research, vol. 383, p. 112482, 2020.

[8]       M. H. Bhatti et al., "Soft computing-based EEG classification by optimal feature selection and neural networks," IEEE Transactions on Industrial Informatics, vol. 15, no. 10, pp. 5747-5754, 2019.

[9]       A. Genkin, D. D. Lewis, and D. Madigan, "Large-scale Bayesian logistic regression for text categorization," technometrics, vol. 49, no. 3, pp. 291-304, 2007.

[10]     R. Anirudh and J. J. Thiagarajan, "Bootstrapping graph convolutional neural networks for autism spectrum disorder classification," in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019: IEEE, pp. 3197-3201. 

[11]     R. Ahsan et al., "Prediction of Autism Severity Level in Bangladesh Using Fuzzy Logic: FIS and ANFIS," in International Conference on Multimedia and Network Information System, 2018: Springer, pp. 201-210. 

[12]     E. Grossi, C. Olivieri, and M. Buscema, "Diagnosis of autism through EEG processed by advanced computational algorithms: a pilot study," Computer methods and programs in biomedicine, vol. 142, pp. 73-79, 2017.

[13]     V. Subbaraju, S. Sundaram, S. Narasimhan, and M. B. Suresh, "Accurate detection of autism spectrum disorder from structural MRI using extended metacognitive radial basis function network," Expert Systems with Applications, vol. 42, no. 22, pp. 8775-8790, 2015.

[14]     J. Kosmicki, V. Sochat, M. Duda, and D. Wall, "Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning," Translational psychiatry, vol. 5, no. 2, pp. e514-e514, 2015.

[15]     T. Takase, S. Oyama, and M. Kurihara, "Effective neural network training with adaptive learning rate based on training loss," Neural networks : the official journal of the International Neural Network Society, vol. 101, pp. 68-78, May 2018, doi: 10.1016/j.neunet.2018.01.016.

[16]     J. Liang, Y. Xu, C. Bao, Y. Quan, and H. Ji, "Barzilai–Borwein-based adaptive learning rate for deep learning," Pattern Recognition Letters, vol. 128, pp. 197-203, 2019, doi: 10.1016/j.patrec.2019.08.029.

[17]     J. Kolbusz, P. Rozycki, O. Lysenko, and B. M. Wilamowski, "Error Back Propagation Algorithm with Adaptive Learning Rate," in 2019 International Conference on Information and Digital Technologies (IDT), 2019: IEEE, pp. 216-222. 

[18]     W. Ahmad, A. Ahmad, C. Lu, B. A. Khoso, and L. Huang, "A novel hybrid decision support system for thyroid disease forecasting," Soft Computing, vol. 22, no. 16, pp. 5377-5383, 2018, doi: 10.1007/s00500-018-3045-9.

[19]     N. M. Nawi et al., "The effect of pre-processing techniques and optimal parameters selection on back propagation neural networks," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 3, pp. 770-777, 2017.

[20]     E. R. Rene, M. E. López, H. S. Park, D. Murthy, and T. Swaminathan, "ANNs for Identifying Shock Loads in Continuously Operated Biofilters: Application to Biological Waste Gas Treatment," in Handbook of Research on Industrial Informatics and Manufacturing Intelligence: Innovations and Solutions: IGI Global, 2012, pp. 72-103.

[21]     S. Sheel, T. Varshney, and R. Varshney, "Accelerated learning in MLP using adaptive learning rate with momentum coefficient," in 2007 International Conference on Industrial and Information Systems, 2007: IEEE, pp. 307-3


Cite this Article as :
Style #
MLA Fatemeh Safara. "Autism Spectrum Diagnosis using Adaptive Learning Algorithm for Multiple MLP Classifier." Journal of Intelligent Systems and Internet of Things, Vol. 2, No. 2, 2021 ,PP. 33-44 (Doi   :  https://doi.org/10.54216/JISIoT.020201)
APA Fatemeh Safara. (2021). Autism Spectrum Diagnosis using Adaptive Learning Algorithm for Multiple MLP Classifier. Journal of Journal of Intelligent Systems and Internet of Things, 2 ( 2 ), 33-44 (Doi   :  https://doi.org/10.54216/JISIoT.020201)
Chicago Fatemeh Safara. "Autism Spectrum Diagnosis using Adaptive Learning Algorithm for Multiple MLP Classifier." Journal of Journal of Intelligent Systems and Internet of Things, 2 no. 2 (2021): 33-44 (Doi   :  https://doi.org/10.54216/JISIoT.020201)
Harvard Fatemeh Safara. (2021). Autism Spectrum Diagnosis using Adaptive Learning Algorithm for Multiple MLP Classifier. Journal of Journal of Intelligent Systems and Internet of Things, 2 ( 2 ), 33-44 (Doi   :  https://doi.org/10.54216/JISIoT.020201)
Vancouver Fatemeh Safara. Autism Spectrum Diagnosis using Adaptive Learning Algorithm for Multiple MLP Classifier. Journal of Journal of Intelligent Systems and Internet of Things, (2021); 2 ( 2 ): 33-44 (Doi   :  https://doi.org/10.54216/JISIoT.020201)
IEEE Fatemeh Safara, Autism Spectrum Diagnosis using Adaptive Learning Algorithm for Multiple MLP Classifier, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 2 , No. 2 , (2021) : 33-44 (Doi   :  https://doi.org/10.54216/JISIoT.020201)