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Abstract #4316

A combinatorial model approach for feature selection from multimodal MRI data

Xiaowei Zhuang1, Virendra Mishra1, Karthik Sreenivasan1, Charles Bernick1, Sarah Banks1, and Dietmar Cordes1,2

1Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States, 2Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, United States

Clinical applications of brain abnormality detection with supervised machine learning techniques are limited due to less and unbalanced sample sizes as compared to rich feature sets in patient population. We proposed a new combinatorial model approach, fs-RBFN, involving sampling from multivariate joint distribution, LASSO feature selection, RBFN cross validation, and inverse probability weighting to solve this problem. The proposed approach was validated against a ground truth phantom and further tested on a multimodal MRI dataset for cognitively impaired and non-impaired professional fighters. Our results suggest superior performance of this technique over several other out-of-the-bag feature selection algorithms.

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