Prediction of Autism Spectrum Disorder using Reliable Ant Colony Optimisation Based Relevant Vector Machine

Prediction of Autism Spectrum Disorder using Reliable Ant Colony Optimisation Based Relevant Vector Machine
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© 2022 by IJETT Journal
Volume-70 Issue-9
Year of Publication : 2022
Authors : S. Malathi, D. Kannan
DOI : 10.14445/22315381/IJETT-V70I9P206

How to Cite?

S. Malathi, D. Kannan, "Prediction of Autism Spectrum Disorder using Reliable Ant Colony Optimisation Based Relevant Vector Machine," International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 57-63, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I9P206

Abstract
Autism spectrum disorder (ASD) is a permanent developmental impairment that impairs a person's capacity to communicate and interact with the outside environment. Social contact and reciprocal communication are consistently impaired in people with ASD. To gain a higher degree of independence, people with ASD require varied amounts of psychosocial assistance, or they may require constant supervision and care at all times. A diagnosis of ASD at an earlier stage leads to more time devoted to individual rehabilitation. This paper proposes a bioinspired optimisation-based classifier, namely Reliable Ant Colony Optimization based Relevant Vector Machine (RACO-RVM), to detect ASD precisely. RACO-RVM performs classification via its heuristic function and pattern evolution. Pheromone update and pattern selection play a vital role in RACO-RVM predicting ASD more accurately. RACO-RVM is evaluated using the benchmark metrics "accuracy" and "F-Measure" on three ASD screening datasets. RACO-RVM is superior in its ability to accurately detect ASD, with an 87.184% averagely compared to other classifiers.

Keywords
Autism, ASD, Optimisation, Classification, ACO, RVM.

Reference
[1] S. Ali Et Al, "A Preliminary Study on Effectiveness of a Standardised Multi-Robot Therapy for Improvement in Collaborative Multi-Human Interaction of Children with Asd," IEEE Access, vol.8, pp.109466–109474, 2020, Doi: 10.1109/Access.2020.3001365.
[2] P. Lu, X. Li, L. Hu, and L. Lu, "Integrating Genomic and Resting State Fmri for Efficient Autism Spectrum Disorder Classification," Multimed. Tools Appl., pp.1–12, 2021, Doi: 10.1007/S11042-020-10473-9.
[3] Y. Zhao, J. Kang, and Q. Long, "Bayesian Multiresolution Variable Selection for Ultra-High Dimensional Neuroimaging Data," IEEE/Acm Trans. Comput. Biol. Bioinforma., vol.15, no.2, pp.537–550, 2018, Doi: 10.1109/Tcbb.2015.2440244.
[4] H. Wu, W.-C. Chen, and N. M. Mayer, "A Face Recognition System That Simulates Perception Impairments of Autistic Children," Neurocomputing, vol.168, pp.770–776, 2015, Doi: Https://Doi.Org/10.1016/J.Neucom.2015.05.047.
[5] M. Quaak, L. Van De Mortel, R. M. Thomas, and G. Van Wingen, "Deep Learning Applications for the Classification of Psychiatric Disorders Using Neuroimaging Data: Systematic Review and Meta-Analysis," Neuroimage Clin., vol.30, pp. 102584, 2021, Doi: Https://Doi.Org/10.1016/J.Nicl.2021.102584.
[6] T. Vargason, E. Roth, G. Grivas, J. Ferina, R. E. Frye, and J. Hahn, "Classification of Autism Spectrum Disorder From Blood Metabolites: Robustness to the Presence of Co-Occurring Conditions," Res. Autism Spectr. Disord., vol.77, pp. 101644, 2020, Doi: Https://Doi.Org/10.1016/J.Rasd.2020.101644.
[7] M. Gök, "A Novel Machine Learning Model to Predict Autism Spectrum Disorders Risk Gene," Neural Comput. Appl., vol.31, no.10, pp.6711–6717, Oct. 2019, Doi: 10.1007/S00521-018-3502-5.
[8] M. T. Tomczak Et Al., "Stress Monitoring System for Individuals with Autism Spectrum Disorders," IEEE Access, vol.8, pp.228236–228244, 2020, Doi: 10.1109/Access.2020.3045633.
[9] H. Chen Et Al., "Multivariate Classification of Autism Spectrum Disorder Using Frequency-Specific Resting-State Functional Connectivity—A Multi-Center Study," Prog. Neuro-Psychopharmacology Biol. Psychiatry, vol.64, pp.1–9, 2016, Doi: Https://Doi.Org/10.1016/J.Pnpbp.2015.06.014.
[10] N. Mohammadian Rad Et Al, "Deep Learning for Automatic Stereotypical Motor Movement Detection Using Wearable Sensors in Autism Spectrum Disorders," Signal Processing, vol.144, pp.180–191, 2018, Doi: Https://Doi.Org/10.1016/J.Sigpro.2017.10.011.
[11] S. S. Meera Et Al., "Towards a Data-Driven Approach to Screen for Autism Risk At 12 Months of Age," J. Am. Acad. Child Adolesc. Psychiatry, 2020, Doi: Https://Doi.Org/10.1016/J.Jaac.2020.10.015.
[12] L. Xu, Y. Guo, J. Li, J. Yu, and H. Xu, "Classification of Autism Spectrum Disorder Based on Fluctuation Entropy of Spontaneous Hemodynamic Fluctuations," Biomed. Signal Process. Control, vol.60, pp. 101958, 2020, Doi: Https://Doi.Org/10.1016/J.Bspc.2020.101958.
[13] R. O. Bahado-Singh Et Al, "Artificial Intelligence Analysis of Newborn Leucocyte Epigenomic Markers for the Prediction of Autism," Brain Res., vol.1724, pp.146457, 2019, Doi: Https://Doi.Org/10.1016/J.Brainres, 2019.146457.
[14] M. Emanuele Et Al., "Motor Synergies: Evidence for a Novel Motor Signature in Autism Spectrum Disorder," Cognition, pp. 104652, 2021, Doi: Https://Doi.Org/10.1016/J.Cognition.2021.104652.
[15] O. Dekhil Et Al., "A Comprehensive Framework for Differentiating Autism Spectrum Disorder From Neurotypicals by Fusing Structural Mri and Resting State Functional Mri," Semin. Pediatr. Neurol., vol.34, pp. 100805, 2020, Doi: Https://Doi.Org/10.1016/J.Spen.2020.100805.
[16] Y. Fu Et Al., "A Novel Pipeline Leveraging Surface-Based Features of Small Subcortical Structures to Classify Individuals with Autism Spectrum Disorder," Prog. Neuro-Psychopharmacology Biol. Psychiatry, vol.104, pp. 109989, 2021, Doi: Https://Doi.Org/10.1016/J.Pnpbp.2020.109989.
[17] J.-W. Sun, R. Fan, Q. Wang, Q.-Q. Wang, X.-Z. Jia, and H.-B. Ma, "Identify Abnormal Functional Connectivity of Resting State Networks in Autism Spectrum Disorder and Apply to Machine Learning-Based Classification," Brain Res., pp. 147299, Jan. 2021, Doi: 10.1016/J.Brainres.2021.147299.
[18] X. Geng, X. Kang, and P. C. M. Wong, "Chapter Four - Autism Spectrum Disorder Risk Prediction: A Systematic Review of Behavioral and Neural Investigations," in Autism, , M. Ilieva and W. K.-W. B. T.-P. in M. B. and T. S. Lau, Eds. Academic Press, vol.173,pp.91–137, 2020.
[19] L. Xu, Q. Hua, J. Yu, and J. Li, "Classification of Autism Spectrum Disorder Based on Sample Entropy of Spontaneous Functional Near Infra-Red Spectroscopy Signal," Clin. Neurophysiol., vol.131, no.6, pp.1365–1374, 2020, Doi: Https://Doi.Org/10.1016/J.Clinph.2019.12.400.
[20] T. M. Epalle, Y. Song, Z. Liu, and H. Lu, "Multi-Atlas Classification of Autism Spectrum Disorder with Hinge Loss Trained Deep Architectures: Abide I Results," Appl. Soft Comput., vol.107, pp. 107375, 2021, Doi: Https://Doi.Org/10.1016/J.Asoc.2021.107375.
[21] M. Li Et Al., "An Automated Assessment Framework for Atypical Prosody and Stereotyped Idiosyncratic Phrases Related to Autism Spectrum Disorder," Comput. Speech Lang., vol.56, pp.80–94, 2019, Doi: Https://Doi.Org/10.1016/J.Csl.2018.11.002.
[22] J. M. Mayor Torres, T. Clarkson, K. M. Hauschild, C. C. Luhmann, M. D. Lerner, and G. Riccardi, "Facial Emotions Are Accurately Encoded in the Neural Signal of Those with Autism Spectrum Disorder: A Deep Learning Approach," Biol. Psychiatry Cogn. Neurosci. Neuroimaging, 2021, Doi: Https://Doi.Org/10.1016/J.Bpsc.2021.03.015.
[23] F. Zhang and H. Roeyers, "Exploring Brain Functions in Autism Spectrum Disorder: A Systematic Review on Functional NearInfrared Spectroscopy (Fnirs) Studies," Int. J. Psychophysiol., vol.137, pp.41–53, 2019, Doi: Https://Doi.Org/10.1016/J.Ijpsycho.2019.01.003.
[24] A. Dickinson Et Al., "Multivariate Neural Connectivity Patterns in Early Infancy Predict Later Autism Symptoms," Biol. Psychiatry Cogn. Neurosci. Neuroimaging, vol.6, no.1, pp.59–69, 2021, Doi: Https://Doi.Org/10.1016/J.Bpsc.2020.06.003.
[25] J. Ramkumar and R. Vadivel, "Performance Modeling of Bio-Inspired Routing Protocols in Cognitive Radio Ad Hoc Network to Reduce End-to-End Delay," Int. J. Intell. Eng. Syst., vol.12, no.1, pp.221–231, 2019, Doi: 10.22266/Ijies2019.0228.22.
[26] J. Ramkumar and R. Vadivel, "Whale Optimization Routing Protocol for Minimising Energy Consumption in Cognitive Radio Wireless Sensor Network," Int. J. Comput. Networks Appl., vol.8, no.4, Doi: 10.22247/Ijcna/2021/209711.
[27] J. Ramkumar and R. Vadivel, "Multi-Adaptive Routing Protocol for Internet of Things Based Ad-Hoc Networks," Wirel. Pers. Commun., pp.1–23, 2021, Doi: 10.1007/S11277-021-08495-Z.
[28] J. Ramkumar and R. Vadivel, "Intelligent Fish Swarm Inspired Protocol (Ifsip) for Dynamic Ideal Routing in Cognitive Radio Ad-Hoc Networks," Int. J. Comput. Digit. Syst., vol.10, no.1, pp.1063–1074, 2020, Doi: Http://Dx.Doi.Org/10.12785/Ijcds/100196.
[29] J. Ramkumar and R. Vadivel, "Improved Frog Leap Inspired Protocol (Iflip) – for Routing in Cognitive Radio Ad Hoc Networks (Crahn)," World J. Eng., vol.15, no.2, pp.306–311, 2018, Doi: 10.1108/Wje-08-2017-0260.
[30] M. Lingaraj, T. N. Sugumar, C. Stanly Felix, and J. Ramkumar, "Query Aware Routing Protocol for Mobility Enabled Wireless Sensor Network," Int. J. Comput. Networks Appl., vol.8, no.3, pp.258, 2021, Doi: 10.22247/Ijcna/2021/209192.
[31] A. Dechsling Et Al., "Virtual Reality and Naturalistic Developmental Behavioral Interventions for Children with Autism Spectrum Disorder," Res. Dev. Disabil., vol.111, pp. 103885, 2021, Doi: Https://Doi.Org/10.1016/J.Ridd.2021.103885.
[32] P. Lanka Et Al., "Malini (Machine Learning in Neuroimaging): A Matlab Toolbox for Aiding Clinical Diagnostics Using RestingState Fmri Data," Data Br., vol.29, pp. 105213, 2020, Doi: Https://Doi.Org/10.1016/J.Dib.2020.105213.
[33] A. M.Mahmoud, F. Alrowais, and H. Karamti, "A Hybrid Deep Contractive Autoencoder and Restricted Boltzmann Machine Approach to Differentiate Representation of Female Brain Disorder," Procedia Comput. Sci., vol.176, pp.1033–1042, 2020, Doi: Https://Doi.Org/10.1016/J.Procs.2020.09.099.
[34] L. Ejlskov Et Al, "Prediction of Autism Risk From Family Medical History Data Using Machine Learning: A National Cohort Study From Denmark," Biol. Psychiatry Glob. Open Sci., 2021, Doi: Https://Doi.Org/10.1016/J.Bpsgos.2021.04.007.
[35] S. Qazi and K. Raza, "Chapter 4 - Fuzzy Logic-Based Hybrid Knowledge Systems for the Detection and Diagnosis of Childhood Autism," H. D. B. T.-H. of D. S. S. for N. D. Jude, Ed. Academic Press, 2021, pp.55–69.
[36] F. F. Thabtah, "Uci Machine Learning Repository: Autism Screening Adult Data Set," 2017. Https://Archive.Ics.Uci.Edu/Ml/Datasets/Autism+Screening+Adult (Accessed Jan. 01, 2021).
[37] F. F. Thabtah, "Uci Machine Learning Repository: Autistic Spectrum Disorder Screening Data for Children Data Set," 2017. Https://Archive.Ics.Uci.Edu/Ml/Datasets/Autistic+Spectrum+Disorder+Screening+Data+for+Children++ (Accessed Jan. 01, 2021).
[38] S. Malathi, D. Kannan, "Adaptive Whale Optimisation Based Support Vector Machine for Prediction of Autism Spectrum Disorder,” Journal of Theoretical and Applied Information Technology, vol.100, no.9, pp.2862 – 2870, 2022.