Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data.

ABSTRACT: The objective of this study is to evaluate machine learning algorithms aimed at predicting surgical treatment outcomes in groups of patients with temporal lobe epilepsy (TLE) using only the structural brain connectome. Specifically, the brain connectome is reconstructed using white matter fiber tracts from presurgical diffusion tensor imaging. To achieve our objective, a two-stage connectome-based prediction framework is developed that gradually selects a small number of abnormal network connections that contribute to the surgical treatment outcome, and in each stage a linear kernel operation is used to further improve the accuracy of the learned classifier. Using a 10-fold cross validation strategy, the first stage in the connectome-based framework is able to separate patients with TLE fr

SUBMITTER: Munsell BC 

PROVIDER: S-EPMC4701213 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature