Detecting Autism spectrum disorder with sailfish optimisation
Autism Spectrum Disorder (ASD), a neurodevelopmental disorder, has been a bottleneck to several clinical researchersdue to data modularization, subjective analysis, and shifts in the accurate prediction of the disorder amongst the samplepopulation. Subjective clinical research suffers from a lengthy procedure, which is a time-consuming process. In this paper,Sailfish Optimization (SFO), a recently developed nature-inspired meta-heuristics optimization algorithm, is being utilizedto detect ASD. The hunting methodology of sailfish inspires SFO. Classical SFO has examined the search space in only onedirection that affects its converging ability. The Random Opposition Based Learning (ROBL) strategy enhances theexploration capacity of SFO and successfully converges the predictive model to global optima. The proposed ROBL-basedSFO (ROBL-SFO) selects relevant features from autism spectrum disorder (child and adult) datasets. According to theresults obtained, the proposed model outperforms the convergence capability and reduces local-optimal stagnation comparedto conventional SFOs.
Autism, Random opposition-based learning, Sailfish optimization
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