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2024 | Vol. 34, no. 4 | 631--645
Tytuł artykułu

Autism spectrum disorder detection in toddlers and adults using deep learning

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Autism spectrum disorder includes symptoms like anxiety, depressive disorders, and epilepsy because of its impact on relationships, learning, and employment. Since no confirmed treatment and diagnosis are available, the emphasis is on improving an individual’s capacities through symptom mitigation. This work investigates autism screening for adults and toddlers utilizing deep learning. We investigated models for feature prediction and fused these predictions with the original dataset to be trained with deep long short-term memory (DLSTM). Features are fused from the training and testing sets and then combined with the original dataset. Data analysis is carried out to detect anomalies and outliers, and a label encoding technique is utilized to convert the categorical data into numerical values. We hyper-tuned the DLSTM model parameters to optimize and assess significant outcomes. Experimental analysis and results revealed that the proposed approach worked better than the other techniques, yielding 99.9% accuracy for toddlers and 99% for adults.
Wydawca

Rocznik
Strony
631--645
Opis fizyczny
Bibliogr. 38 poz., rys., tab., wykr.
Twórcy
autor
  • Department of Computer Science, COMSATS University Islamabad, Comsats University Rd, Punjab, 57000, Sahiwal, Pakistan, sidraabbas@ieee.org
autor
  • Department of Electrical and Computer Engineering, Anderson University, 316 Boulevard, Anderson, 29621, South Carolina, USA, sojo@andersonuniversity.edu
  • Faculty of Computing and Information, Al-Baha University, P.O. Box 1988, 65799, Al-Zahir, Saudi Arabia, moez.krichen@redcad.org
  • ReDCAD Laboratory, University of Sfax, Route de Soukra km 3.5, Sfax, 3038, Tunisia
  • Department of Information Technology, Princess Nourah bint Abdul Rahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia, meaalamro@pnu.edu.sa
  • Department of Management Information Systems, Qassim University, P.O. Box 6640, Buraidah, 51452, Saudi Arabia, a.mihoub@qu.edu.sa
Bibliografia
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  • [8] Baizer, J.S. (2024). Neuroanatomy of autism: What is the role of the cerebellum?, Cerebral Cortex 34(13): 94-103.
  • [9] Bala, M., Ali, M.H., Satu, M.S., Hasan, K.F. and Moni, M.A. (2022). Efficient machine learning models for early stage detection of autism spectrum disorder, Algorithms 15(5): 166.
  • [10] Barik, K., Watanabe, K., Bhattacharya, J. and Saha, G. (2023). A fusion-based machine learning approach for autism detection in young children using magnetoencephalography signals, Journal of Autism and Developmental Disorders 53(12): 4830-4848.
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  • [14] Chen, G. (2016). A gentle tutorial of recurrent neural network with error backpropagation, arXiv: 1610.02583.
  • [15] Chen, T. and Guestrin, C. (2016). XGBoost: A scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, pp. 785-794.
  • [16] Deng, X., Zhang, J., Liu, R. and Liu, K. (2022). Classifying ASD based on time-series FMRI using spatial-temporal transformer, Computers in Biology and Medicine 151: 106320.
  • [17] Farooq, M.S., Tehseen, R., Sabir, M. and Atal, Z. (2023). Detection of autism spectrum disorder (ASD) in children and adults using machine learning, Scientific Reports 13(1): 9605.
  • [18] Francese, R. and Yang, X. (2022). Supporting autism spectrum disorder screening and intervention with machine learning and wearables: A systematic literature review, Complex & Intelligent Systems 8(5): 3659-3674.
  • [19] Garg, A., Parashar, A., Barman, D., Jain, S., Singhal, D., Masud, M. and Abouhawwash, M. (2022). Autism spectrum disorder prediction by an explainable deep learning approach, Computers, Materials & Continua 71(1): 1459-1471.
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  • [22] Islam, M.Z., Islam, M.M. and Asraf, A. (2020). A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images, Informatics in Medicine Unlocked 20: 100412.
  • [23] Kanhirakadavath, M.R. and Chandran, M.S.M. (2022). Investigation of eye-tracking scan path as a biomarker for autism screening using machine learning algorithms, Diagnostics 12(2): 518.
  • [24] Lu, A. and Perkowski, M. (2021). Deep learning approach for screening autism spectrum disorder in children with facial images and analysis of ethnoracial factors in model development and application, Brain Sciences 11(11): 1446.
  • [25] Mohammad, U.G., Imtiaz, S., Shakya, M., Almadhor, A. and Anwar, F. (2022). An optimized feature selection method using ensemble classifiers in software defect prediction for healthcare systems, Wireless Communications and Mobile Computing 2022(1): 1028175.
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Typ dokumentu
Bibliografia
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Identyfikator YADDA
bwmeta1.element.baztech-11ffd316-e615-41f0-a8c3-50adae7f2fed
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