Effects of hardware heterogeneity on the performance of SVM Alzheimer's disease classifier.

ABSTRACT: Fully automated machine learning methods based on structural magnetic resonance imaging (MRI) data can assist radiologists in the diagnosis of Alzheimer's disease (AD). These algorithms require large data sets to learn the separation of subjects with and without AD. Training and test data may come from heterogeneous hardware settings, which can potentially affect the performance of disease classification. A total of 518 MRI sessions from 226 healthy controls and 191 individuals with probable AD from the multicenter Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to investigate whether grouping data by acquisition hardware (i.e. vendor, field strength, coil system) is beneficial for the performance of a support vector machine (SVM) classifier, compared to the case where data fr

SUBMITTER: Abdulkadir A 

PROVIDER: S-EPMC3258661 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature