Machine learning on high dimensional shape data from subcortical brain surfaces: A comparison of feature selection and classification methods

Benjamin S.C. Wade, Shantanu H. Joshi, Boris A. Gutman, Paul M. Thompson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 6th International Workshop, MLMI 2015 Held in Conjunction with MICCAI 2015, Proceedings
EditorsLuping Zhou, Yinghuan Shi, Li Wang, Qian Wang
PublisherSpringer Verlag
Pages36-43
Number of pages8
ISBN (Print)9783319248875
DOIs
StatePublished - 2015
Externally publishedYes
Event6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: 5 Oct 20155 Oct 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9352
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015
Country/TerritoryGermany
CityMunich
Period5/10/155/10/15

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