Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network
Main Article Content
Abstract
Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D CNNs have shown improved accuracy in the classification of ASD compared to traditional machine learning algorithms, on all these datasets with higher accuracy of 99.45%, 98.66%, and 90% for Autistic Spectrum Disorder Screening in Data for Adults, Children, and Adolescents respectively as they are better suited for the analysis of time series data commonly used in the diagnosis of this disorder
Received 10/02/2023,
Revised 23/04/2023,
Accepted 25/04/2023,
Published 20/06/2023
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
Voinsky I, Fridland OY, Aran A, Frye RE, Gurwitz D. Machine Learning-Based Blood RNA Signature for Diagnosis of Autism Spectrum Disorder. Int J Mol Sci. 2023; 24(3): 2082. https://dx.doi.org/10.3390/ijms24032082
Song DY, Topriceanu CC, Ilie-Ablachim DC, Kinali M, Bisdas S. Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis. Neuroradiology. 2021; 63: 2057–72. https://dx.doi.org/10.1007/s00234-021-02774-z
Minissi ME, Chicchi Giglioli IA, Mantovani F, Alcaniz Raya M. Assessment of the autism spectrum disorder based on machine learning and social visual attention: A systematic review. J Autism Dev Disord. 2022; 52(5): 2187–202. https://dx.doi.org/10.1007/s10803-021-05106-5
Lai M, Lee J, Chiu S, Charm J, So WY, Yuen FP, et al. A machine learning approach for retinal images analysis as an objective screening method for children with autism spectrum disorder. E Clinical Medicine. 2020; 28: 100588. https://dx.doi.org/10.1016/j.eclinm.2020.100588
Mellema CJ, Nguyen KP, Treacher A, Montillo A. Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning. Sci Rep. 2022; 12(1): 3057. https://dx.doi.org/10.1038/s41598-022-06459-2
Devika Varshini G, Chinnaiyan R. Optimized machine learning classification approaches for prediction of autism spectrum disorder. Ann Autism Dev Disord 2020; 1(1): 1001. https://www.remedypublications.com/annals-of-autism-developmental-disorders-abstract.php?aid=5957
Nafea AA, Omar N, AL-Ani MM. Adverse Drug Reaction Detection Using Latent Semantic Analysis. J Comput Sci. 2021; 17(10): 960–70. https://dx.doi.org/10.3844/jcssp.2021.960.970
Li Y, Mache MA, Todd TA. Automated identification of postural control for children with autism spectrum disorder using a machine learning approach. J Biomech. 2020; 113: 110073. https://dx.doi.org/10.1016/j.jbiomech.2020.110073
Li X, Lu R, Wang Q, Wang J, Duan X, Sun Y, et al. One-dimensional convolutional neural network (1D-CNN) image reconstruction for electrical impedance tomography. Rev Sci Instrum. 2020; 91(12): 124704. https://dx.doi.org/10.1063/5.0025881
Ma D, Shang L, Tang J, Bao Y, Fu J, Yin J. Classifying breast cancer tissue by Raman spectroscopy with one-dimensional convolutional neural network. Spectrochim Acta Part A Mol Biomol Spectrosc. 2021; 256: 119732. https://dx.doi.org/10.1016/j.saa.2021.119732
Gaonkar S, Dessai NF, Costa J, Borkar A, Aswale S, Shetgaonkar P. A survey on botnet detection techniques. In: 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). IEEE; 2020. p. 1–6. https://dx.doi.org/10.1109/ic-ETITE47903.2020.Id-70
Sun JW, Fan R, Wang Q, Wang QQ, Jia XZ, Ma HB. Identify abnormal functional connectivity of resting state networks in Autism spectrum disorder and apply to machine learning-based classification. Brain Res. 2021; 1757: 147299. https://dx.doi.org/10.1016/j.brainres.2021.147299
Raj S, Masood S. Analysis and detection of autism spectrum disorder using machine learning techniques. Procedia Comput Sci. 2020; 167: 994–1004. https://dx.doi.org/10.1016/j.procs.2020.03.399
Hossain MD, Kabir MA, Anwar A, Islam MZ. Detecting autism spectrum disorder using machine learning techniques: An experimental analysis on toddler, child, adolescent and adult datasets. Heal Inf Sci Syst. 2021; 9: 1–13. https://dx.doi.org/10.1007/s13755-021-00145-9
Kumar CJ, Das PR. The diagnosis of ASD using multiple machine learning techniques. Int J Dev Disabil. 2022; 68(6): 973–83. https://dx.doi.org/10.1080/20473869.2021.1933730
Mashudi NA, Ahmad N, Noor NM. Classification of adult autistic spectrum disorder using machine learning approach. IAES Int J Artif Intell. 2021; 10(3): 743. https://dx.doi.org/10.11591/ijai.v10.i3.pp743-751
Alwidian J, Elhassan A, Ghnemat R. Predicting autism spectrum disorder using machine learning technique. Int J Recent Technol Eng. 2020; 8(5): 4139–43. https://dx.doi.org/10.35940/ijrte.E6016.018520
Heinsfeld AS, Franco AR, Craddock RC, Buchweitz A, Meneguzzi F. Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage Clin. 2018; 17: 16–23. https://dx.doi.org/10.1016/j.nicl.2017.08.017
AL-Ani MM, Omar N, Nafea AA. A Hybrid Method of Long Short-Term Memory and Auto-Encoder Architectures for Sarcasm Detection. J Comput Sci. 2021; 17(11): 1093–8. https://dx.doi.org/10.3844/jcssp.2021.1093.1098
Mohammed BA, Al-Shareeda MA, Manickam S, Al-Mekhlafi ZG, Alreshidi A, Alazmi M, et al. FC-PA: Fog Computing-based Pseudonym Authentication Scheme in 5G-enabled Vehicular Networks. IEEE Access. 2023, 11: 18571-18581. https://dx.doi.org/10.1109/ACCESS.2023.3247222.
Mahmood RAR, Abdi A, Hussin M. Performance evaluation of intrusion detection system using selected features and machine learning classifiers. Baghdad Sci J. 2021; 18(2): 884–98. https://dx.doi.org/10.21123/bsj.2021.18.2(Suppl.).0884
Rabani ST, Khan QR, Khanday A. Detection of suicidal ideation on Twitter using machine learning & ensemble approaches. Baghdad Sci J. 2020; 17(4): 1328. https://dx.doi.org/10.21123/bsj.2020.17.4.1328
Bassel A, Abdulkareem AB, Alyasseri ZAA, Sani NS, Mohammed HJ. Automatic malignant and benign skin cancer classification using a hybrid deep learning approach. Diagnostics. 2022; 12(10): 2472. https://dx.doi.org/10.3390/diagnostics12102472
Hassan FH, Omar MA. Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation. Baghdad Sci J. 2021; 18(4): 1406. https://dx.doi.org/10.21123/bsj.2021.18.4(Suppl.).1406
Abdulmajeed AA, Tawfeeq TM, Al-jawaherry MA. Constructing a Software Tool for Detecting Face Mask-wearing by Machine Learning. Baghdad Sci J. 2022; 19(3): 642. https://dx.doi.org/10.21123/bsj.2022.19.3.0642
Al-Mekhlafi ZG, Al-Shareeda MA, Manickam S, Mohammed BA, Qtaish A. Lattice-Based Lightweight Quantum Resistant Scheme in 5G-Enabled Vehicular Networks. Mathematics. 2023; 11(2): 399. https://dx.doi.org/10.3390/math11020399
Alsaade FW, Alzahrani MS. Classification and detection of autism spectrum disorder based on deep learning algorithms. Comput Intell Neurosci. 2022; 2022: 1-10. https://dx.doi.org/10.1155/2022/8709145
Wang H, Avillach P. Diagnostic classificati;on and prognostic prediction using common genetic variants in autism spectrum disorder: Genotype-based deep learning. JMIR Med inform. 2021; 9(4): e24754. https://dx.doi.org/10.2196/24754
Yang X, Schrader PT, Zhang N. A deep neural network study of the ABIDE repository on autism spectrum classification. Int J Adv Comput Sci Appl. 2020; 11(4): 1-6. https://dx.doi.org/10.14569/IJACSA.2020.0110401
Murari A, Lungaroni M, Rossi R, Spolladore L, Gelfusa M. Complexity: Frontiers in Data-Driven Methods for Understanding, Prediction, and Control of Complex Systems 2022 on the Development of Information Theoretic Model Selection Criteria for the Analysis of Experimental Data. Complexity. 2022; 2022. https://dx.doi.org/10.1155/2022/9518303
Almuqhim F, Saeed F. ASD-SAENet: a sparse autoencoder, and deep-neural network model for detecting autism spectrum disorder (ASD) using fMRI data. Front Comput Neurosci. 2021; 15: 654315. https://dx.doi.org/10.3389/fncom.2021.654315