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Abstract #3508

Performance evaluation of machine learning algorithms for multiple sclerosis phenotype classification using 7-Tesla MRI and clinical features

Seongjin Choi1 and Daniel M Harrison1,2
1Neurology, University of Maryland School of Medicine, Baltimore, MD, United States, 2Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States

Three machine-learning algorithms were evaluated in the multiple sclerosis phenotype classification of a relatively small cohort. High accuracy of multiple-sclerosis phenotype classification was achievable by applying tree-based ensemble methods to integrated 7T MRI and clinical data features. Feature integration did not guarantee performance improvements in all machine learning algorithms evaluated. Features considered important may vary depending on the classification algorithm used.

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