Machine Learning on High Dimensional Shape Data from Subcortical Brain Surfaces: A Comparison of Feature Selection and Classification Methods

Recently, high-dimensional shape data (HDSD) has been demonstrated to be informative in describing subcortical brain morphometry in several disorders. While HDSD may serve as a biomarker of disease, its high dimensionality may require careful treatment in its application to machine learning. Here, we compare several possible approaches for feature selection and pattern classification using HDSD. We explore the efficacy of three candidate feature selection (FS) methods: Guided Random Forest (GRF), LASSO and no feature selection (NFS). Each feature set was applied to three classifiers: Random Forest (RF), Support Vector Machines (SVM) and Naïve Bayes (NB). Each model was cross-validated using two diagnostic contrasts: Alzheimer’s Disease and mild cognitive impairment; each relative to matched controls. GRF and NFS outperformed LASSO as FS methods and were comparably competitive. NB underperformed relative to RF and SVM, which were comparable in performance. Our results advocate the NFS-RF approach for its speed, simplicity and interpretability.