Autism Spectrum Disorder Classification Using Machine Learning and Deep Learning- A Survey

Authors

DOI:

https://doi.org/10.4108/eetpht.9.4240

Keywords:

ASD, CNN, Machine Learning, DBN

Abstract

Modern, highly developed technology has impacted reputable procedures in the medical and healthcare industries. Smart healthcare prediction to the senior sick patient is not only for quick access to data but also to get dependable treatment in an accurate prediction by healthcare service provider. smart health prediction helps in the identification of numerous diseases. Based on patient experience, Deep learning technology provides a robust application space in the medical sector for health disease prediction problems by applying deep learning techniques to analyze various symptoms. In order to classify things and make precise predictions about diseases, deep learning techniques are utilized. people's health will be more secure, medical care will be of a higher caliber, and personal information will be kept more secret. As deep learning algorithms become more widely used to construct an interactive smart healthcare prediction and evaluation model on the basis of the deep learning model, CNN is upgraded. Advanced deep learning algorithms combined with multi-mode approaches and resting-state functional magnetic resonance represent an innovative approach that researchers have taken. A DL structure for the programmed ID ASD using highlights separated from the corpus callosum and cerebrum volume from the Stand dataset is proposed. Imaging is used to reveal hidden diseased brain connectome patterns to find diagnostic and prognostic indicators.

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References

Wingfield, Benjamin, et al. “A predictive model for pediatric autism screening.” Health informatics journal vol. 26,4 (2020): 2538-2553. doi:10.1177/1460458219887823. DOI: https://doi.org/10.1177/1460458219887823

Shahamiri, Seyed Reza, and Fadi Thabtah. "Autism AI: a new autism screening system based on artificial intelligence." Cognitive Computation 12.4 (2020): 766-777. DOI: https://doi.org/10.1007/s12559-020-09743-3

Wang, Haishuai & Avillach, Paul. (2020). Autism Spectrum Disorders Classification using Genotype Data: A Deep Learning-based Predictive Classifier (Preprint). JMIR Medical Informatics. 9. 10.2196/24754. DOI: https://doi.org/10.2196/24754

Haebich, Kristina. “Understanding autism spectrum disorder and social functioning in children with neurofibromatosis type 1: protocol for a cross-sectional multimodal study.” BMJ open vol. 9,9 e030601. 26 Sep. 2019. DOI: https://doi.org/10.1136/bmjopen-2019-030601

Vaishnavi Konjeti, P.S. Geethanjali, Ananya Polisetty, T. Kusumita, N. Sai Baba, VHS.Chaitanya, Vegetable Rot Indicator and Preservator(V-RIP), 9th National Conference on Advancements in Information Technology, 3rd May 2023.

Pan, Zhao-Yu, et al. "Beneficial effects of repeated washed microbiota transplantation in children with autism." Frontiers in Pediatrics (2022): 971. DOI: https://doi.org/10.3389/fped.2022.928785

Ahmed ElGazzar; Rajat Thomas; Guido van Wingen; "Benchmarking Graph Neural Networks for FMRI Analysis", ARXIV-CS.LG, 2022.

XinDeng; Jiahao, Zhang; Rui, Liu; Ke, Liu; "Classifying ASD Based on Time-series FMRI Using Spatial-temporal Transformer", COMPUTERS IN BIOLOGY AND, MEDICINE, Volume 151, Part B,2022,106320 DOI: https://doi.org/10.1016/j.compbiomed.2022.106320

M. K. Waghmare, M. Malhotra and R. S R, "Quantitative Analysis of EEG Neurofeedback using optimized 1-DPSO," 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 2023, pp. 373-378, DOI: https://doi.org/10.1109/SPIN57001.2023.10116248

Reeja, S. R., Rino Cherian, and Kiran Waghmare. "EEG signal-based human emotion detection using an artificial neural network." Handbook of Decision Support Systems for Neurological Disorders. Academic Press, 2021. 107-124. DOI: https://doi.org/10.1016/B978-0-12-822271-3.00007-4

V. Kavitha and R. Siva, "Classification of Toddler, Child, Adolescent and Adult for Autism Spectrum Disorder Using Machine Learning Algorithm," 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2023, pp. 2444-2449. DOI: https://doi.org/10.1109/ICACCS57279.2023.10112932

Sartipi, Shadi, Mahrokh G. Shayesteh, and Hashem Kalbkhani. "Diagnosing of autism spectrum disorder based on GARCH variance series for rs-fMRI data." 2018 9th International Symposium on Telecommunications (IST). IEEE, 2018. DOI: https://doi.org/10.1109/ISTEL.2018.8661147

S. Mounika and R. S. R, "Comprehensive Study on RS_FMRI and EEG Using Deep Learning Approach for Brain Stroke," 2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), Bengaluru, India, 2023, pp. 384-388, doi: 10.1109/IITCEE57236.2023.10090864. DOI: https://doi.org/10.1109/IITCEE57236.2023.10090864

Amirali Kazeminejad; Roberto C Sotero; "Topological Properties Of Resting-State FMRI Functional Networks Improve Machine Learning-Based Autism Classification", FRONTIERS IN NEUROSCIENCE, VOLUME=12 2019. 18. Lu Z, Wang J, Mao R, Lu M, Shi J. Jointly Composite Feature Learning and Autism Spectrum Disorder Classification Using Deep Multi-Output Takagi-Sugeno-Kang Fuzzy Inference Systems. IEEE/ACM Trans Comput Biol Bioinform. 2023 Jan-Feb;20(1):476-488. Epub 2023 Feb 3. PMID: 35349448.

Huang, Zhi-An, et al. "Federated multi-task learning for joint diagnosis of multiple mental disorders on MRI scans." IEEE Transactions on Biomedical Engineering vol. 70,4 (2023): 1137-1149. (2022). DOI: https://doi.org/10.1109/TBME.2022.3210940

Pranavesh Kumar Talupuri, Aarush Shivkumar, Gangadhar K., R. Anmol, Hriday Shah, Sai Eeshwar D. CRISP : Comprehensive Route Information System for Passengers, 9th National Conference on Advancements in Information Technology, 3rd May 2023.

Hemanth Ajay Kumar Posina, CRYPTOCURRENCY PREDICTION USING SENTIMENT ANALYSIS, 9th National Conference on Advancements in Information Technology, 3rd May 2023.

Hao, Xiaoke, et al. "Exploring high-order correlations with deep-broad learning for autism spectrum disorder diagnosis." Frontiers in Neuroscience VOLUME=16 2022. DOI: https://doi.org/10.3389/fnins.2022.1046268

Ahmed ZAT, Aldhyani THH, Jadhav ME, Alzahrani MY, Alzahrani ME, Althobaiti MM, Alassery F, Alshaflut A, Alzahrani NM, Al-Madani AM. Facial Features Detection System to Identify Children with Autism Spectrum Disorder: Deep Learning Models. Comput Math Methods Med. 2022 Apr 4;2022:3941049. PMID: 35419082; PMCID: PMC9001065. DOI: https://doi.org/10.1155/2022/3941049

Alsaade FW, Alzahrani MS. Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms. Comput Intel Neurosci. 2022 Feb 28;2022:8709145.PMID: 35265118; PMCID: PMC8901307. DOI: https://doi.org/10.1155/2022/8709145

Ali Rashid “Autism spectrum Disorder Diagnosis Using Face Features based on Deep Learning “NeuoQuantology August 2022 volume 20, Issue10,Page 9140-9151.

K. Vikas, K. V. Goud, S. Ponaganti, A. N. Reddy, K. Nagasai and S. N. Mohanty, "Agricultural Land Classification Based on Machine Learning Algorithms," 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2022, pp. 1-4, doi: 10.1109/ICCCNT54827.2022.9984254. DOI: https://doi.org/10.1109/ICCCNT54827.2022.9984254

Shetkar, R.M., Mohanty, S.N. (2021). Mid-Brain Connective for Human Information Processing: A New Strategy for the Science of Optimal Decision Making. In: Nandan Mohanty, S. (eds) Decision Making And Problem Solving. Springer, Cham. https://doi.org/10.1007/978-3-030-66869-3_4 DOI: https://doi.org/10.1007/978-3-030-66869-3_4

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Published

26-10-2023

How to Cite

1.
S R R, Mounika S. Autism Spectrum Disorder Classification Using Machine Learning and Deep Learning- A Survey. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Oct. 26 [cited 2024 May 25];9. Available from: https://publications.eai.eu/index.php/phat/article/view/4240