Prediction of Autism Spectrum Disorder using Reliable Ant Colony Optimisation Based Relevant Vector Machine
|© 2022 by IJETT Journal|
|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
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.
Autism, ASD, Optimisation, Classification, ACO, RVM.
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