Autism Spectrum Disorder Screening Using Discriminative Brain Sub-Networks: An Entropic Approach
This work addresses autism screening for medical diagnosis by improving classification accuracy using brain network analysis, but it is incremental as it builds on existing methods with new feature extraction.
The study tackled autism spectrum disorder screening by using a genetic algorithm to extract discriminative brain sub-networks and training machine learning models on entropy features, achieving maximum accuracies of 73.1% for structural networks and 82.2% for functional networks on the UCLA dataset, and 66.1% on the ABIDE dataset.
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.