Meeting Banner
Abstract #0225

Perils in the Use of Cross-validation for Performance Estimation in Neuroimaging-based Diagnostic Classification

Pradyumna Lanka1, D Rangaprakash1, and Gopikrishna Deshpande1,2,3

1AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States, 2Department of Psychology, Auburn University, Auburn, AL, United States, 3Alabama Advanced Imaging Consortium, Auburn University and University of Alabama, Birmingham, AL, United States

In this study, we highlight the fact that cross-validation accuracy might not be a good measure of performance estimation in neuroimaging-based diagnostic classification, especially with smaller sample sizes typically encountered in neuroimaging. We trained an array of classifiers using resting state fMRI-based functional connectivity measures from subjects in a particular age group using cross-validation, and then tested on an independent set of subjects with the same diagnosis (mild cognitive impairment and Alzheimer’s disease), but from a different age group. We demonstrate that cross-validation accuracy might give us an inflated estimate of the true performance of the classifiers.

This abstract and the presentation materials are available to members only; a login is required.

Join Here