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Comparative study of machine learning and deep learning methods on ASD classification

arXiv 2209.08601 ↗First posted 18 Sep 2022eess.IV cs.LG q-bio.NCcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 56 references that are in the map. First posted on arXiv 18 Sep 2022.

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References · 12

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  12. 2018Incorporating Prior Information with Fused Sparse Group Lasso: Application to Prediction of Clinical Measures from Neuroimagescited by 1

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