Prediction of autism levels in children using inverse firefly optimization of support vector machine classifier

Abstract

Autism Spectrum Disorder (ASD) is the most common developmental delay in children nowadays. The identification of
autism is ambitious and the studies reveal that it can be accomplished based on symptoms. One of the imperative research
issues is the diagnosis and classification of autism spectrum disorder. The aim is intended to predict the level of autism
using support vector machine. Support vector machine (SVM) is the most widely used machine learning technique for
classification. The accuracy and efficiency of the SVM classifier mainly depend on the tuning of SVM parameters. In this
paper, the parameters of SVM are optimized by proposed Firefly algorithm to improve the efficacy of the classifier. The
proposed method uses an Inverse Firefly algorithm (IFFA) to optimize SVM parameters and it is applied to the
classification of autism spectrum disorder. The experimental results of the proposed method attain noble results when
compared to other state-of-the-art techniques. The results stipulate that the proposed SVM-IFFA method can be affirmed as
a proficient machine learning technique for accurate diagnosis of autism spectrum disorder.