  The autism dataset is studied to identify the differences between autistic
and healthy groups. For this, the resting-state Functional Magnetic Resonance
Imaging (rs-fMRI) data of the two groups are analyzed, and networks of
connections between brain regions were created. Several classification
frameworks are developed to distinguish the connectivity patterns between the
groups. The best models for statistical inference and precision were compared,
and the tradeoff between precision and model interpretability was analyzed.
Finally, the classification accuracy measures were reported to justify the
performance of our framework. Our best model can classify autistic and healthy
patients on the multisite ABIDE I data with 71% accuracy.
