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 Cordes^1,2

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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