  Autism is one of the most important neurological disorders which leads to
problems in a person's social interactions. Improvement of brain imaging
technologies and techniques help us to build brain structural and functional
networks. Finding networks topology pattern in each of the groups (autism and
healthy control) can aid us to achieve an autism disorder screening model. In
the present study, we have utilized the genetic algorithm to extract a
discriminative sub-network that represents differences between two groups
better. In the fitness evaluation phase, for each sub-network, a machine
learning model was trained using various entropy features of the sub-network
and its performance was measured. Proper model performance implies extracting a
good discriminative sub-network. Network entropies can be used as network
topological descriptors. The evaluation results indicate the acceptable
performance of the proposed screening method based on extracted discriminative
sub-networks and the machine learning models succeeded in obtaining a maximum
accuracy of 73.1% in structural networks of the UCLA dataset, 82.2% in
functional networks of the UCLA dataset, and 66.1% in functional networks of
ABIDE datasets.
