In the Netherlands, 3% of people above 4 years old are diagnosed with autism. Diagnosing is currently done with a psychological assessment, but classifying people with autism using resting state functional magnetic imaging, or rs-fMRI, has become promising. The goal of this project was to see if new features could be found, based on the graph of rs-fMRI data stored in the Autism Brain Imaging Data Exchange (ABIDE) dataset, that had a significant positive influence on the accuracy of a predictive model. To do this, several feature selection modules were researched and coded in Python. Subsequently, these were tested with the features and classification methods created by our partner subgroups. The best performing model, using all data, had an accuracy of 74.26% and a sensitivity of 65.35%. The best performing model using graph features, on all data, had an accuracy of 60.4% and a sensitivity of 46.6%. This indicates that there were no graph features developed that had a significant positive influence on classifying whether someone has autism.