Extracting a Discriminative Structural Sub-Network for ASD Screening using the Evolutionary Algorithm
This provides an incremental improvement for ASD screening using neuroimaging, potentially aiding in more accurate diagnosis.
The paper tackled the problem of diagnosing autism spectrum disorder (ASD) by extracting a discriminative structural sub-network from brain imaging data using an evolutionary algorithm, achieving an average accuracy of 76% for screening.
Autism spectrum disorder (ASD) is one of the most significant neurological disorders that disrupt a person's social communication skills. The progression and development of neuroimaging technologies has made structural network construction of brain regions possible. In this paper, after finding the discriminative sub-network using the evolutionary algorithm, the simple features of the sub-network lead us to diagnose autism in various subjects with plausible accuracy (76% on average). This method yields substantially better results compared to previous researches. Thus, this method may be used as an accurate assistance in autism screening