A Comparative Study of Machine Learning Methods on ASD Classification

• Activity Number: 334 - Network Data and Models
• Type: Contributed
• Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Ramchandra Rimal*
• Companies: Middle Tennessee State University
• Keywords: supervised learning; fMRI data; ASD classification; LSTM; GRU; brain imaging

Abstract

This article works with the autism dataset 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 the 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.